Bumblekite2024_tutorial_1_participants_version.ipynb

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        "# Bumblekite Tutorial 1: Hidden Hypoxemia's Impact on In-Hospital Mortality\n",
        "\n",
        "\n",
        "* Understanding the Problem: Pulse oximeters are medical devices used to measure the level of oxygen in the blood without needing a blood sample. The most accurate method, however, requires taking a blood sample to measure oxygen levels directly. Pulse oximeters currently available are less accurate in people with darker skin tones. Theses pulse oximetry inaccuracies can fail to detect episodes of hidden hypoxemia, i.e., low SaO2 with high SpO2. Hidden hypoxemias can result in less treatment and increased mortality. Yet flawed, pulse oximeters remain ubiquitously used because of their ease of use; debiasing the underlying algorithms could alleviate the downstream repercussions of hidden hypoxemia.\n",
        "\n",
        "* Dataset: BOLD is a new comprehensive dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients.\n",
        "\n",
        "* Main Paper: The tutorial is build upon the paper titled \"Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality\" published in JAMA Network Open. This paper investigates discrepancies between pulse oximetry (SpO2) and arterial oxygen saturation (SaO2) measurements, examining the impact of these discrepancies on clinical outcomes across different racial and ethnic groups.\n",
        "\n",
        "\n",
        "* Objective of the tutorial: The objective of this tutorial is to provide a comprehensive guide on how to analyze and address biases in healthcare data, with a specific focus on hidden hypoxemia and its impact on in-hospital mortality predictions. Through a series of structured steps, including exploratory data analysis, data preprocessing, train-test splitting, and model evaluation, participants will learn how to:\n",
        "\n",
        "1. Detect and understand hidden patterns and biases in healthcare datasets.\n",
        "2. Preprocess data effectively to ensure robust and reliable model performance.\n",
        "3. Evaluate the influence of hidden hypoxemia on patient outcomes using statistical and machine learning techniques.\n",
        "4. Assess and compare the performance of different models across various racial groups to highlight the importance of careful feature selection and bias mitigation in clinical predictions.\n",
        "\n",
        "\n",
        "* Key Variables to keep in mind:\n",
        "\n",
        "1. **Pulse Oximetry (SpO2)**: This is a [ non-invasive ] method used to measure the oxygen level (oxygen saturation) in the blood. It is usually done using a device clipped onto a finger, toe, or earlobe. Normal SpO2 levels range from 95% to 100%, indicating sufficient oxygen in the blood. Levels below 95% can indicate hypoxemia, with severe hypoxemia occurring below 85%.\n",
        "2. **Arterial Oxygen Saturation (SaO2)**: This is an [ invasive ] measurement of oxygen saturation directly from the blood using an arterial blood gas (ABG) test. It is more accurate than pulse oximetry but requires drawing blood from an artery. SaO2 provides a direct measurement of oxygen levels in the blood and is used to confirm the accuracy of SpO2 readings, especially in cases of suspected hypoxemia.\n",
        "3. **ABG**: Arterial Blood Gas\n",
        "4. **Hypoxemia**: This refers to low levels of oxygen in the blood. It can be dangerous and requires medical attention.\n",
        "5. **Hidden Hypoxemia**: This occurs when a patient's pulse oximetry reading (SpO2) suggests they have normal oxygen levels, but their arterial oxygen saturation (SaO2) shows they actually have low oxygen levels. → (ie, SpO2 ≥ 88% but SaO2 <88%).\n",
        "6. **Organ Dysfunction**: This refers to the impaired function of organs (like the heart, lungs, liver, or kidneys) often assessed using specific scores like the Sequential Organ Failure Assessment (SOFA) score.\n",
        "7. **In-hospital Mortality**: This means death occurring during a hospital stay.\n",
        "8. **Sequential Organ Failure Assessment (SOFA) Score**: A scoring system used to track a patient's status during their stay in an intensive care unit (ICU). Higher scores indicate more severe organ dysfunction.\n",
        "9. **Electronic Health Record (EHR)**: Digital version of patients' paper charts. They contain the medical and treatment history of patients.\n",
        "\n",
        "\n",
        "Note: A lot of the content in this tutorial was inspired from theses previous datathons: MIT Critical Datathon 2023 and Brown University Datathon 2024.  \n"
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      "source": [
        "**👥 Pairing Team**\n",
        "\n",
        "`Add your names here (alphabetically)`\n",
        "\n",
        "- ....\n",
        "- ....\n"
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      "source": [
        "import sklearn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
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      "source": [
        "# Loading the Data"
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      "cell_type": "code",
      "source": [
        "# Mount Google Drive to access your files:\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
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      "source": [
        "# After ensuring that your CSV file is placed in your Google Drive, run:\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "df_initial = pd.read_csv(\"/content/bold_dataset.csv\")"
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              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3fa7f3b0-5f1d-4c75-b84c-cdac5dde5c67 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_initial"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Did it work? Inspect the Data:\n",
        "\n",
        "# Peak at our data (First 5 rows)\n",
        "df_initial.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "ced8ab43",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ced8ab43",
        "outputId": "6205d8fd-c7a3-4ff8-9cab-11b2b9d7267e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 49093 entries, 0 to 49092\n",
            "Columns: 142 entries, unique_subject_id to sofa_future_renal_24hr\n",
            "dtypes: bool(1), float64(122), int64(7), object(12)\n",
            "memory usage: 52.9+ MB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# Get a summary of the DataFrame\n",
        "print(df_initial.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "abd2af73-8e5c-44a8-af0d-7873a45e2389",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abd2af73-8e5c-44a8-af0d-7873a45e2389",
        "outputId": "a13e43b5-25da-4e66-e37e-4cf48341c836"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column names: ['unique_subject_id', 'unique_hospital_admission_id', 'unique_icustay_id', 'subject_id', 'hospital_admission_id', 'icustay_id', 'source_db', 'hospitalid', 'numbedscategory', 'teachingstatus', 'region', 'admission_age', 'sex_female', 'weight_admission', 'height_admission', 'BMI_admission', 'datetime_hospital_admit', 'datetime_hospital_discharge', 'datetime_icu_admit', 'datetime_icu_discharge', 'los_hospital', 'los_ICU', 'comorbidity_score_name', 'comorbidity_score_value', 'in_hospital_mortality', 'race_ethnicity', 'SaO2_timestamp', 'pH', 'pCO2', 'pO2', 'SaO2', 'SpO2', 'Carboxyhemoglobin', 'Methemoglobin', 'SpO2_timestamp', 'delta_SpO2', 'delta_vitals_heart_rate', 'vitals_heart_rate', 'delta_vitals_resp_rate', 'vitals_resp_rate', 'delta_vitals_mbp_ni', 'vitals_mbp_ni', 'delta_vitals_sbp_ni', 'vitals_sbp_ni', 'delta_vitals_dbp_ni', 'vitals_dbp_ni', 'delta_vitals_mbp_i', 'vitals_mbp_i', 'delta_vitals_sbp_i', 'vitals_sbp_i', 'delta_vitals_dbp_i', 'vitals_dbp_i', 'delta_vitals_tempc', 'vitals_tempc', 'delta_cbc_hemoglobin', 'cbc_hemoglobin', 'delta_cbc_hematocrit', 'cbc_hematocrit', 'delta_cbc_mch', 'cbc_mch', 'delta_cbc_mchc', 'cbc_mchc', 'delta_cbc_mcv', 'cbc_mcv', 'delta_cbc_platelet', 'cbc_platelet', 'delta_cbc_rbc', 'cbc_rbc', 'delta_cbc_rdw', 'cbc_rdw', 'delta_cbc_wbc', 'cbc_wbc', 'delta_coag_fibrinogen', 'coag_fibrinogen', 'delta_coag_inr', 'coag_inr', 'delta_coag_pt', 'coag_pt', 'delta_coag_ptt', 'coag_ptt', 'delta_bmp_sodium', 'bmp_sodium', 'delta_bmp_potassium', 'bmp_potassium', 'delta_bmp_chloride', 'bmp_chloride', 'delta_bmp_bicarbonate', 'bmp_bicarbonate', 'delta_bmp_bun', 'bmp_bun', 'delta_bmp_creatinine', 'bmp_creatinine', 'delta_bmp_glucose', 'bmp_glucose', 'delta_bmp_aniongap', 'bmp_aniongap', 'delta_bmp_calcium', 'bmp_calcium', 'delta_bmp_lactate', 'bmp_lactate', 'delta_hfp_alt', 'hfp_alt', 'delta_hfp_alp', 'hfp_alp', 'delta_hfp_ast', 'hfp_ast', 'delta_hfp_bilirubin_total', 'hfp_bilirubin_total', 'delta_hfp_bilirubin_direct', 'hfp_bilirubin_direct', 'delta_hfp_albumin', 'hfp_albumin', 'delta_others_ck_cpk', 'others_ck_cpk', 'delta_others_ck_mb', 'others_ck_mb', 'delta_others_ld_ldh', 'others_ld_ldh', 'delta_sofa_past_overall_24hr', 'sofa_past_overall_24hr', 'delta_sofa_past_coagulation_24hr', 'sofa_past_coagulation_24hr', 'delta_sofa_past_liver_24hr', 'sofa_past_liver_24hr', 'delta_sofa_past_cardiovascular_24hr', 'sofa_past_cardiovascular_24hr', 'delta_sofa_past_cns_24hr', 'sofa_past_cns_24hr', 'delta_sofa_past_renal_24hr', 'sofa_past_renal_24hr', 'delta_sofa_future_overall_24hr', 'sofa_future_overall_24hr', 'delta_sofa_future_coagulation_24hr', 'sofa_future_coagulation_24hr', 'delta_sofa_future_liver_24hr', 'sofa_future_liver_24hr', 'delta_sofa_future_cardiovascular_24hr', 'sofa_future_cardiovascular_24hr', 'delta_sofa_future_cns_24hr', 'sofa_future_cns_24hr', 'delta_sofa_future_renal_24hr', 'sofa_future_renal_24hr']\n"
          ]
        }
      ],
      "source": [
        "# Get the column names\n",
        "column_names = df_initial.columns.tolist()\n",
        "print (\"Column names:\", column_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "94909564-ad68-4993-871a-ad57efcff817",
      "metadata": {
        "id": "94909564-ad68-4993-871a-ad57efcff817"
      },
      "source": [
        "For an overview of all the variables and their meanings, please refer to the paper on the dataset [BOLD: Blood-gas and Oximetry Linked Dataset](https://www.nature.com/articles/s41597-024-03225-z#Sec23) . Check the \"Description of fields\" section for detailed information.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "bde53039",
      "metadata": {
        "id": "bde53039"
      },
      "outputs": [],
      "source": [
        "# List of key variables to explore further\n",
        "key_variables = [\n",
        "\n",
        "    'source_db', 'comorbidity_score_value',\n",
        "\n",
        "    # ids\n",
        "    'unique_subject_id', 'unique_hospital_admission_id', 'unique_icustay_id',\n",
        "\n",
        "    # patient\n",
        "    'admission_age', 'sex_female', 'race_ethnicity', 'BMI_admission', # BMI: Body Mass Index\n",
        "    'weight_admission' , 'height_admission',\n",
        "\n",
        "    # ICU stay info\n",
        "    'sofa_past_overall_24hr', 'sofa_future_overall_24hr',\n",
        "\n",
        "    # Vital signs\n",
        "    'vitals_heart_rate',  'vitals_mbp_ni', 'vitals_resp_rate', 'vitals_tempc',\n",
        "        ## MBP: Mean Blood Measure\n",
        "        ## invasive(i) and non invasive(ni)\n",
        "        ## DBP: Diastolic Blood Pressure\n",
        "\n",
        "    # LABS\n",
        "    'cbc_wbc', 'cbc_hematocrit', 'cbc_platelet',\n",
        "    'coag_fibrinogen', 'coag_inr',\n",
        "    'bmp_sodium', 'bmp_potassium', 'bmp_chloride', 'bmp_bicarbonate', 'bmp_bun', 'bmp_creatinine',\n",
        "    'bmp_glucose', 'bmp_aniongap', 'bmp_calcium', 'bmp_lactate', 'hfp_alt', 'hfp_alp', 'hfp_ast',\n",
        "    'hfp_bilirubin_total', 'hfp_albumin', 'others_ck_cpk',\n",
        "    'others_ld_ldh',\n",
        "\n",
        "    # Target\n",
        "    'in_hospital_mortality',\n",
        "\n",
        "     # ABG data\n",
        "    'pH',\n",
        "\n",
        "    # Study Features\n",
        "    'SpO2', 'SaO2',\n",
        "\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "090ce2de",
      "metadata": {
        "id": "090ce2de"
      },
      "outputs": [],
      "source": [
        "# Filter the dataset to only focus on the key variables relevant to the tutorial\n",
        "df_key_variables = df_initial [key_variables]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "f4d253f5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4d253f5",
        "outputId": "d76927c5-328d-4e31-a914-4c15fc3e45cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['source_db', 'comorbidity_score_value', 'unique_subject_id', 'unique_hospital_admission_id', 'unique_icustay_id', 'admission_age', 'sex_female', 'race_ethnicity', 'BMI_admission', 'weight_admission', 'height_admission', 'sofa_past_overall_24hr', 'sofa_future_overall_24hr', 'vitals_heart_rate', 'vitals_mbp_ni', 'vitals_resp_rate', 'vitals_tempc', 'cbc_wbc', 'cbc_hematocrit', 'cbc_platelet', 'coag_fibrinogen', 'coag_inr', 'bmp_sodium', 'bmp_potassium', 'bmp_chloride', 'bmp_bicarbonate', 'bmp_bun', 'bmp_creatinine', 'bmp_glucose', 'bmp_aniongap', 'bmp_calcium', 'bmp_lactate', 'hfp_alt', 'hfp_alp', 'hfp_ast', 'hfp_bilirubin_total', 'hfp_albumin', 'others_ck_cpk', 'others_ld_ldh', 'in_hospital_mortality', 'pH', 'SpO2', 'SaO2']\n"
          ]
        }
      ],
      "source": [
        "# Visualize the columns of the new dataframe\n",
        "print(df_key_variables.columns.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "f8683b9b-a5ff-4b00-ac41-39165434815f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8683b9b-a5ff-4b00-ac41-39165434815f",
        "outputId": "63dd6e33-533e-40f2-d1ab-d6009b6d5dc6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(49093, 43)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df_key_variables.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "ed03adb8-549b-4fee-8f58-d5118154f045",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ed03adb8-549b-4fee-8f58-d5118154f045",
        "outputId": "1c2dd91d-d831-4533-af25-b3d3ade9981e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "race_ethnicity\n",
            "White                                 76.141201\n",
            "Black                                  9.746807\n",
            "Unknown                                7.255617\n",
            "Hispanic OR Latino                     4.310187\n",
            "Asian                                  1.747703\n",
            "American Indian / Alaska Native        0.774041\n",
            "Native Hawaiian / Pacific Islander     0.018333\n",
            "More Than One Race                     0.006111\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Calculate the percentage of each unique value in the 'race_ethnicity' column\n",
        "race_ethnicity_counts = df_key_variables['race_ethnicity'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Display the percentages\n",
        "print(race_ethnicity_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "63696c39-2cf1-433e-a308-3c31195fde23",
      "metadata": {
        "id": "63696c39-2cf1-433e-a308-3c31195fde23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7421d05d-4707-461f-c02d-b368d2c6b6c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "race_ethnicity\n",
              "White                 37380\n",
              "Black                  4785\n",
              "Hispanic OR Latino     2116\n",
              "Asian                   858\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Filter the Dataframe to include only the most common racial/ethnic groups\n",
        "# Please add your code here ...\n",
        "# [\"White\", \"Black\", \"Hispanic OR Latino\", \"Asian\"]\n",
        "df_filtered = df_key_variables[df_key_variables[\"race_ethnicity\"].isin([\"White\", \"Black\", \"Hispanic OR Latino\", \"Asian\"])]\n",
        "df_filtered[\"race_ethnicity\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CG0Xkl2m5_tf",
        "outputId": "0fa6d614-edda-4e30-ab31-4d1ba61a4854"
      },
      "id": "CG0Xkl2m5_tf",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45139, 43)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93158db1-7327-42ce-8602-ed9cedec70fa",
      "metadata": {
        "id": "93158db1-7327-42ce-8602-ed9cedec70fa"
      },
      "source": [
        "# Train Test Split\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "669d8e78-ef62-413e-bd33-5dd6c1043f48",
      "metadata": {
        "id": "669d8e78-ef62-413e-bd33-5dd6c1043f48"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "data_train, data_test = train_test_split (df_filtered, test_size = 0.2, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "7c05eec7-1787-414e-89d7-591426d9130a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7c05eec7-1787-414e-89d7-591426d9130a",
        "outputId": "d677cdd7-fc9f-440d-95bf-740038172d80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the training data (36111, 43)\n",
            "Shape of the testing data (9028, 43)\n"
          ]
        }
      ],
      "source": [
        "print(\"Shape of the training data\", data_train.shape)\n",
        "print(\"Shape of the testing data\", data_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b60fc9ca-39c9-4517-8032-f1befc390e9d",
      "metadata": {
        "id": "b60fc9ca-39c9-4517-8032-f1befc390e9d"
      },
      "source": [
        "### Check balancing of the mortality outcome"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Question to the participants:\n",
        "  - What is the purpose of this step?\n",
        "  - How should we go about it?\n",
        "\n",
        "\n",
        "* Your Answer: ..."
      ],
      "metadata": {
        "id": "lhTUIbD7bOJv"
      },
      "id": "lhTUIbD7bOJv"
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "f2373388-e01c-4797-8fbc-2fe97031a9b1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2373388-e01c-4797-8fbc-2fe97031a9b1",
        "outputId": "550c9455-3746-4fab-dee8-b8d4e5401eff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.17536571747627025\n",
            "0.17472150096322975\n",
            "0.17794150480017862\n"
          ]
        }
      ],
      "source": [
        "# Please add your code here ...\n",
        "\n",
        "print(df_filtered[\"in_hospital_mortality\"].mean())\n",
        "print(data_train[\"in_hospital_mortality\"].mean())\n",
        "print(data_test[\"in_hospital_mortality\"].mean())\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8fbaae74-a52c-40eb-85ae-288735b0baed",
      "metadata": {
        "id": "8fbaae74-a52c-40eb-85ae-288735b0baed"
      },
      "source": [
        "- Question: Any observations?\n",
        "- Your answer: ...."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initial Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "jIiiqdthcA8e"
      },
      "id": "jIiiqdthcA8e"
    },
    {
      "cell_type": "markdown",
      "id": "91599ec6-1b0d-458c-bce6-aa2e3836e427",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "id": "91599ec6-1b0d-458c-bce6-aa2e3836e427"
      },
      "source": [
        "## Pandas profiling\n",
        "\n",
        "Pandas-profiling delivers an extended analysis of a DataFrame. Really helpful!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "dcad9f37-e698-4c93-949d-9ec00806d6d1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dcad9f37-e698-4c93-949d-9ec00806d6d1",
        "outputId": "7caf5fdc-7473-4940-9bc1-83a228bf7c10"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "source_db                        object\n",
            "comorbidity_score_value         float64\n",
            "unique_subject_id                 int64\n",
            "unique_hospital_admission_id      int64\n",
            "unique_icustay_id                 int64\n",
            "admission_age                   float64\n",
            "sex_female                        int64\n",
            "race_ethnicity                   object\n",
            "BMI_admission                   float64\n",
            "weight_admission                float64\n",
            "height_admission                float64\n",
            "sofa_past_overall_24hr          float64\n",
            "sofa_future_overall_24hr        float64\n",
            "vitals_heart_rate               float64\n",
            "vitals_mbp_ni                   float64\n",
            "vitals_resp_rate                float64\n",
            "vitals_tempc                    float64\n",
            "cbc_wbc                         float64\n",
            "cbc_hematocrit                  float64\n",
            "cbc_platelet                    float64\n",
            "coag_fibrinogen                 float64\n",
            "coag_inr                        float64\n",
            "bmp_sodium                      float64\n",
            "bmp_potassium                   float64\n",
            "bmp_chloride                    float64\n",
            "bmp_bicarbonate                 float64\n",
            "bmp_bun                         float64\n",
            "bmp_creatinine                  float64\n",
            "bmp_glucose                     float64\n",
            "bmp_aniongap                    float64\n",
            "bmp_calcium                     float64\n",
            "bmp_lactate                     float64\n",
            "hfp_alt                         float64\n",
            "hfp_alp                         float64\n",
            "hfp_ast                         float64\n",
            "hfp_bilirubin_total             float64\n",
            "hfp_albumin                     float64\n",
            "others_ck_cpk                   float64\n",
            "others_ld_ldh                   float64\n",
            "in_hospital_mortality           float64\n",
            "pH                              float64\n",
            "SpO2                            float64\n",
            "SaO2                            float64\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# Inspect the data types of each column\n",
        "print(data_train.dtypes)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install pandas-profiling"
      ],
      "metadata": {
        "collapsed": true,
        "id": "tYU01ixnn7Wf"
      },
      "id": "tYU01ixnn7Wf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4b99c7aa-54a0-4975-9fa3-3deff2f64d56",
      "metadata": {
        "id": "4b99c7aa-54a0-4975-9fa3-3deff2f64d56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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          ]
        },
        "outputId": "df78dcff-5596-436e-ce64-e64c981c8ba0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "51deddc2abc44aec9440adafbbf2b542",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1545e9e6d323422fb7150293cfc251fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a621c809cd014aeca2ea97e2a94ef252"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from ydata_profiling import ProfileReport\n",
        "\n",
        "# Generate the profile report\n",
        "profile = ProfileReport(data_train, title=\"Pandas Profiling Report of the Training Data\")\n",
        "\n",
        "# To display the report in a Jupyter notebook\n",
        "profile.to_notebook_iframe()\n",
        "\n",
        "# To save the report to an HTML file\n",
        "# profile.to_file(\"path_of_where_ProfileReport_HTML_file_should_be_saved\")\n",
        "profile.to_file(\"/content/profile/data_train_copy_report.html\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note: You can download the HTML file from your Drive to your local computer and view it there!"
      ],
      "metadata": {
        "id": "x7gsmqczej90"
      },
      "id": "x7gsmqczej90"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### The Profile Report will take around 10 mins to run, so while it is loading....\n",
        "\n",
        "\n",
        "* Think of the question: Why do you think we are doing the train test split right after loading the data specifically? Why not before or after?\n",
        "* Your answer: ....\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Now that we did our train test split, pre processing & EDA should be done solely on train set!"
      ],
      "metadata": {
        "id": "M_25LYq4tWGm"
      },
      "id": "M_25LYq4tWGm"
    },
    {
      "cell_type": "markdown",
      "id": "3d51746e",
      "metadata": {
        "id": "3d51746e"
      },
      "source": [
        "## Overview of the data using Tableone\n",
        "Goal: To understand the distribution of patients across different racial-ethnic groups, sex, and other demographics. This step sets the foundation by showing the diversity in the dataset and highlighting potential areas of interest for further analysis."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "profile"
      ],
      "metadata": {
        "id": "84GpXvPrnFCH"
      },
      "id": "84GpXvPrnFCH",
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "profile.to_file(\"/content/data_train_copy_report.html\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bda8616beedb41389887b96f11e66466",
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            "240de3552ca54ff9bf226c4ba8d61a2a"
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        },
        "id": "J1u9BU_Fm9WR",
        "outputId": "d3addae6-7918-4d7b-8500-550f35e8ef16"
      },
      "id": "J1u9BU_Fm9WR",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bda8616beedb41389887b96f11e66466"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "b6219d71-3d52-4800-a0cc-70d145b06474",
      "metadata": {
        "id": "b6219d71-3d52-4800-a0cc-70d145b06474"
      },
      "outputs": [],
      "source": [
        "# Create a new column with 'F' for female and 'M' for male\n",
        "data_train['sex'] = data_train['sex_female'].apply(lambda x: 'F' if x == 1 else 'M')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered[df_filtered[\"admission_age\"] < 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99
        },
        "id": "j0_oqFd-oJj8",
        "outputId": "9042738e-1795-408c-ab51-e62da006656a"
      },
      "id": "j0_oqFd-oJj8",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [source_db, comorbidity_score_value, unique_subject_id, unique_hospital_admission_id, unique_icustay_id, admission_age, sex_female, race_ethnicity, BMI_admission, weight_admission, height_admission, sofa_past_overall_24hr, sofa_future_overall_24hr, vitals_heart_rate, vitals_mbp_ni, vitals_resp_rate, vitals_tempc, cbc_wbc, cbc_hematocrit, cbc_platelet, coag_fibrinogen, coag_inr, bmp_sodium, bmp_potassium, bmp_chloride, bmp_bicarbonate, bmp_bun, bmp_creatinine, bmp_glucose, bmp_aniongap, bmp_calcium, bmp_lactate, hfp_alt, hfp_alp, hfp_ast, hfp_bilirubin_total, hfp_albumin, others_ck_cpk, others_ld_ldh, in_hospital_mortality, pH, SpO2, SaO2]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4c5c2ea-9cc9-4a9f-b06b-c19f7c6a14ff\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>source_db</th>\n",
              "      <th>comorbidity_score_value</th>\n",
              "      <th>unique_subject_id</th>\n",
              "      <th>unique_hospital_admission_id</th>\n",
              "      <th>unique_icustay_id</th>\n",
              "      <th>admission_age</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>race_ethnicity</th>\n",
              "      <th>BMI_admission</th>\n",
              "      <th>weight_admission</th>\n",
              "      <th>...</th>\n",
              "      <th>hfp_alp</th>\n",
              "      <th>hfp_ast</th>\n",
              "      <th>hfp_bilirubin_total</th>\n",
              "      <th>hfp_albumin</th>\n",
              "      <th>others_ck_cpk</th>\n",
              "      <th>others_ld_ldh</th>\n",
              "      <th>in_hospital_mortality</th>\n",
              "      <th>pH</th>\n",
              "      <th>SpO2</th>\n",
              "      <th>SaO2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>0 rows × 43 columns</p>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d4c5c2ea-9cc9-4a9f-b06b-c19f7c6a14ff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d4c5c2ea-9cc9-4a9f-b06b-c19f7c6a14ff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "6f7c76a7",
      "metadata": {
        "id": "6f7c76a7"
      },
      "outputs": [],
      "source": [
        "# Create Age Groups:\n",
        "bins = [0,65,float('inf')] # Specifies the edges of the bins for the age groups (0 to 65, and 65 and above).\n",
        "labels = ['<65', '>=65'] # Specifies the labels for the age groups.\n",
        "data_train ['age_group'] = pd.cut(data_train['admission_age'], bins = bins, labels = labels) # Creates a new column age_group in the DataFrame that categorizes admission_age into the specified age ranges."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "b38d863c-d736-41e5-810f-148619659471",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b38d863c-d736-41e5-810f-148619659471",
        "outputId": "1c3ba81c-564c-46e5-e148-c6a559b0b57a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12807     <65\n",
              "2209      <65\n",
              "13681    >=65\n",
              "24674    >=65\n",
              "20590     <65\n",
              "         ... \n",
              "12244     <65\n",
              "48586    >=65\n",
              "41069     <65\n",
              "881      >=65\n",
              "16930     <65\n",
              "Name: age_group, Length: 36111, dtype: category\n",
              "Categories (2, object): ['<65' < '>=65']"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "data_train ['age_group']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install tableone"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RiEqxqizAbi",
        "outputId": "4e3ede0d-4c79-4a13-8e02-bbba53eeca01"
      },
      "id": "6RiEqxqizAbi",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tableone\n",
            "  Downloading tableone-0.9.1-py3-none-any.whl (41 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/41.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.6/41.6 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: jinja2>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from tableone) (3.1.4)\n",
            "Requirement already satisfied: numpy>=1.19.1 in /usr/local/lib/python3.10/dist-packages (from tableone) (1.25.2)\n",
            "Requirement already satisfied: openpyxl>=3.1.2 in /usr/local/lib/python3.10/dist-packages (from tableone) (3.1.4)\n",
            "Requirement already satisfied: pandas>=2.0.3 in /usr/local/lib/python3.10/dist-packages (from tableone) (2.0.3)\n",
            "Requirement already satisfied: scipy>=1.10.1 in /usr/local/lib/python3.10/dist-packages (from tableone) (1.11.4)\n",
            "Requirement already satisfied: statsmodels>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from tableone) (0.14.2)\n",
            "Requirement already satisfied: tabulate>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from tableone) (0.9.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=3.1.4->tableone) (2.1.5)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.10/dist-packages (from openpyxl>=3.1.2->tableone) (1.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.3->tableone) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.3->tableone) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.3->tableone) (2024.1)\n",
            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.14.1->tableone) (0.5.6)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.14.1->tableone) (24.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.6->statsmodels>=0.14.1->tableone) (1.16.0)\n",
            "Installing collected packages: tableone\n",
            "Successfully installed tableone-0.9.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "81ef8e37",
      "metadata": {
        "id": "81ef8e37"
      },
      "outputs": [],
      "source": [
        "from tableone import TableOne\n",
        "\n",
        "# Specify the columns to include in the table\n",
        "columns = ['admission_age',  'age_group' , 'BMI_admission', 'race_ethnicity', 'sex', 'in_hospital_mortality', 'coag_fibrinogen', \"others_ck_cpk\", \"others_ld_ldh\", \"bmp_lactate\"]\n",
        "\n",
        "\n",
        "# Categorical Variables [represent categories]\n",
        "categorical = ['sex', 'race_ethnicity', 'age_group']\n",
        "\n",
        "\n",
        "# Define how to group the data for comparison\n",
        "# In this case grouping by 'in_hospital_mortality' to compare different groups based on their mortality status\n",
        "# Optional: Try to group by another outcome and see what you can get !! e.g 'race_ethnicity'\n",
        "groupby = 'in_hospital_mortality'\n",
        "\n",
        "# Indicates which variables are not normally distributed\n",
        "nonnormal = ['BMI_admission']\n",
        "\n",
        "# Renames the variables for better readability\n",
        "labels = {\n",
        "    'in_hospital_mortality': 'Mortality',\n",
        "    'admission_age': 'Age',\n",
        "    'race_ethnicity': 'Race and Ethnicity',\n",
        "    'BMI_admission': 'BMI',\n",
        "    'sex': 'Sex',\n",
        "    'age_group': 'Age Group'\n",
        "}\n",
        "\n",
        "# Sets the order for categorical variables\n",
        "order = {\n",
        "    'sex': ['F', 'M'],  # 1 for Female, 0 for Male\n",
        "    'age_group': ['<65','>=65']  # Example age groups\n",
        "}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "id": "c2fee132",
      "metadata": {
        "id": "c2fee132",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "outputId": "3de639b5-1462-4586-e395-7e60fba8e305"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                              Grouped by Mortality                                                                       \n",
              "                                                           Missing              Overall                  0.0                  1.0 P-Value\n",
              "n                                                                                 36111                29559                 6258        \n",
              "Age, mean (SD)                                                   0        64.64 (15.68)        63.80 (15.70)        68.63 (14.94)  <0.001\n",
              "Age Group, n (%)           <65                                            17268 (47.82)        14772 (49.97)         2359 (37.70)  <0.001\n",
              "                           >=65                                           18843 (52.18)        14787 (50.03)         3899 (62.30)        \n",
              "BMI, median [Q1,Q3]                                           1370  28.04 [23.78,33.59]  28.24 [24.02,33.82]  27.17 [22.86,32.54]  <0.001\n",
              "Race and Ethnicity, n (%)  Asian                                             700 (1.94)           559 (1.89)           126 (2.01)   0.680\n",
              "                           Black                                           3860 (10.69)         3168 (10.72)          662 (10.58)        \n",
              "                           Hispanic OR Latino                               1689 (4.68)          1372 (4.64)           309 (4.94)        \n",
              "                           White                                          29862 (82.70)        24460 (82.75)         5161 (82.47)        \n",
              "Sex, n (%)                 F                                              15836 (43.85)        12925 (43.73)         2782 (44.46)   0.298\n",
              "                           M                                              20275 (56.15)        16634 (56.27)         3476 (55.54)        \n",
              "coag_fibrinogen, mean (SD)                                   30422      272.57 (141.82)      265.89 (132.66)      313.86 (184.06)  <0.001\n",
              "others_ck_cpk, mean (SD)                                     27068     922.26 (7070.89)     920.47 (7418.48)     941.18 (5781.77)   0.896\n",
              "others_ld_ldh, mean (SD)                                     33115     529.16 (1172.20)      445.76 (977.84)     808.85 (1638.75)  <0.001\n",
              "bmp_lactate, mean (SD)                                       19331          2.73 (2.70)          2.31 (2.04)          4.30 (3.98)  <0.001"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"5\" halign=\"left\">Grouped by Mortality</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Missing</th>\n",
              "      <th>Overall</th>\n",
              "      <th>0.0</th>\n",
              "      <th>1.0</th>\n",
              "      <th>P-Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n</th>\n",
              "      <th></th>\n",
              "      <td></td>\n",
              "      <td>36111</td>\n",
              "      <td>29559</td>\n",
              "      <td>6258</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age, mean (SD)</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>64.64 (15.68)</td>\n",
              "      <td>63.80 (15.70)</td>\n",
              "      <td>68.63 (14.94)</td>\n",
              "      <td>&lt;0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Age Group, n (%)</th>\n",
              "      <th>&lt;65</th>\n",
              "      <td></td>\n",
              "      <td>17268 (47.82)</td>\n",
              "      <td>14772 (49.97)</td>\n",
              "      <td>2359 (37.70)</td>\n",
              "      <td>&lt;0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>&gt;=65</th>\n",
              "      <td></td>\n",
              "      <td>18843 (52.18)</td>\n",
              "      <td>14787 (50.03)</td>\n",
              "      <td>3899 (62.30)</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI, median [Q1,Q3]</th>\n",
              "      <th></th>\n",
              "      <td>1370</td>\n",
              "      <td>28.04 [23.78,33.59]</td>\n",
              "      <td>28.24 [24.02,33.82]</td>\n",
              "      <td>27.17 [22.86,32.54]</td>\n",
              "      <td>&lt;0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Race and Ethnicity, n (%)</th>\n",
              "      <th>Asian</th>\n",
              "      <td></td>\n",
              "      <td>700 (1.94)</td>\n",
              "      <td>559 (1.89)</td>\n",
              "      <td>126 (2.01)</td>\n",
              "      <td>0.680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Black</th>\n",
              "      <td></td>\n",
              "      <td>3860 (10.69)</td>\n",
              "      <td>3168 (10.72)</td>\n",
              "      <td>662 (10.58)</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hispanic OR Latino</th>\n",
              "      <td></td>\n",
              "      <td>1689 (4.68)</td>\n",
              "      <td>1372 (4.64)</td>\n",
              "      <td>309 (4.94)</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>White</th>\n",
              "      <td></td>\n",
              "      <td>29862 (82.70)</td>\n",
              "      <td>24460 (82.75)</td>\n",
              "      <td>5161 (82.47)</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sex, n (%)</th>\n",
              "      <th>F</th>\n",
              "      <td></td>\n",
              "      <td>15836 (43.85)</td>\n",
              "      <td>12925 (43.73)</td>\n",
              "      <td>2782 (44.46)</td>\n",
              "      <td>0.298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td></td>\n",
              "      <td>20275 (56.15)</td>\n",
              "      <td>16634 (56.27)</td>\n",
              "      <td>3476 (55.54)</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>coag_fibrinogen, mean (SD)</th>\n",
              "      <th></th>\n",
              "      <td>30422</td>\n",
              "      <td>272.57 (141.82)</td>\n",
              "      <td>265.89 (132.66)</td>\n",
              "      <td>313.86 (184.06)</td>\n",
              "      <td>&lt;0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>others_ck_cpk, mean (SD)</th>\n",
              "      <th></th>\n",
              "      <td>27068</td>\n",
              "      <td>922.26 (7070.89)</td>\n",
              "      <td>920.47 (7418.48)</td>\n",
              "      <td>941.18 (5781.77)</td>\n",
              "      <td>0.896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>others_ld_ldh, mean (SD)</th>\n",
              "      <th></th>\n",
              "      <td>33115</td>\n",
              "      <td>529.16 (1172.20)</td>\n",
              "      <td>445.76 (977.84)</td>\n",
              "      <td>808.85 (1638.75)</td>\n",
              "      <td>&lt;0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_lactate, mean (SD)</th>\n",
              "      <th></th>\n",
              "      <td>19331</td>\n",
              "      <td>2.73 (2.70)</td>\n",
              "      <td>2.31 (2.04)</td>\n",
              "      <td>4.30 (3.98)</td>\n",
              "      <td>&lt;0.001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br />"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "name_of_table = TableOne(\n",
        "    data_train,  # The dataset to summarize\n",
        "    columns=columns,\n",
        "    categorical=categorical,\n",
        "    groupby=groupby,\n",
        "    nonnormal=nonnormal,\n",
        "    rename=labels,\n",
        "    order=order,\n",
        "    # limit=limit,\n",
        "    overall=True,  # Whether to include overall summary statistics\n",
        "    missing=True,  # Whether to include info about the number of missing values for each variable\n",
        "    pval=True,  # Whether to include p-values for comparisons\n",
        "    decimals=2  # Number of decimal places for numerical values\n",
        ")\n",
        "\n",
        "\n",
        "# Display the table\n",
        "display(name_of_table)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can create your own TableOne table with the variables of your choice!"
      ],
      "metadata": {
        "id": "n43XVmMVf3n-"
      },
      "id": "n43XVmMVf3n-"
    },
    {
      "cell_type": "markdown",
      "id": "4b73e664-71f2-47f0-89c4-6da4835092b2",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "id": "4b73e664-71f2-47f0-89c4-6da4835092b2"
      },
      "source": [
        "### Understanding the table:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "- Columns & Rows:\n",
        "  * Missing: Number of missing values for each variable.\n",
        "  * Overall: Shows the count and percentage of each subgroup in the entire dataset.\n",
        "  * 0.0 (No Mortality): Shows the count and percentage of each subgroup within the group that did not experience mortality.\n",
        "  * 1.0 (Mortality): Shows the count and percentage of each subgroup within the group that experienced mortality.\n",
        "  * P-Value: Indicates whether there is a statistically significant difference between the distributions of the subgroups across the mortality categories.\n",
        "\n",
        "- Variables:\n",
        "  * admission_age, mean (SD): Mean age at admission with standard deviation (SD). SD measures the amount of variation or dispersion of ages.\n",
        "  * BMI, median [Q1, Q3]: Median Body Mass Index (BMI) with the first quartile (Q1) and third quartile (Q3). Median represents the middle value, and quartiles divide the data into four equal parts.\n",
        "  * Race and Ethnicity, n (%): Number and percentage of patients in different racial and ethnic groups.\n",
        "  * Sex, n (%): Number and percentage of male and female patients.\n",
        "\n",
        "- Grouped by Mortality:\n",
        "  * Statistics for Subgroups: Compares characteristics of patients who survived (0.0) versus those who died (1.0) during their hospital stay.\n",
        "  * P-Value: Indicates whether differences between the mortality groups are statistically significant. A low p-value (typically < 0.05) suggests significant differences.\n",
        "\n",
        "\n",
        "- e.g. Interpreting the Percentages:\n",
        "  * In the overall dataset, Black individuals make up around 10%.\n",
        "  * If you sum up all the percentages in the row of 0.0 for each separate demographic variable, you will get 100%."
      ],
      "metadata": {
        "id": "fXf_p7yvji0A"
      },
      "id": "fXf_p7yvji0A"
    },
    {
      "cell_type": "markdown",
      "id": "4e1838ea-5c5b-4443-8d5c-369f0c43afa5",
      "metadata": {
        "id": "4e1838ea-5c5b-4443-8d5c-369f0c43afa5"
      },
      "source": [
        "### Interpretation of the table:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: Can you comment on the table?\n",
        "- Your Answer: ...."
      ],
      "metadata": {
        "id": "8nfp7abuiEmN"
      },
      "id": "8nfp7abuiEmN"
    },
    {
      "cell_type": "markdown",
      "id": "88b12e56",
      "metadata": {
        "id": "88b12e56"
      },
      "source": [
        "# Pre Processing"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d8b0b3ec-9c88-4cb3-83dd-6931f4e603d0",
      "metadata": {
        "id": "d8b0b3ec-9c88-4cb3-83dd-6931f4e603d0"
      },
      "source": [
        "## Handling Missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "feed0e8d-bc50-4959-a08d-61b07fec84f1",
      "metadata": {
        "id": "feed0e8d-bc50-4959-a08d-61b07fec84f1"
      },
      "outputs": [],
      "source": [
        "# Filter Data for Unique Patients to ensure each patient has a unique record\n",
        "df_filtered = data_train.drop_duplicates(subset='unique_subject_id', keep='first')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4fbff25b-4c14-4f48-bc54-fe5b0306ee28",
      "metadata": {
        "id": "4fbff25b-4c14-4f48-bc54-fe5b0306ee28"
      },
      "source": [
        "Why? The goal is to ensure that each patient is represented by only one record in the dataset. This is important for statistical analyses where each data point should be independent. Multiple records for the same patient can introduce bias and inaccuracies in the analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "b12bd2e8-761a-424b-9c0d-9a38b9e33165",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b12bd2e8-761a-424b-9c0d-9a38b9e33165",
        "outputId": "b9cc6f3d-9f20-460b-d4fc-f2a1f5921f78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of missing values per column:\n",
            " source_db                        0.000000\n",
            "comorbidity_score_value          0.005538\n",
            "unique_subject_id                0.000000\n",
            "unique_hospital_admission_id     0.000000\n",
            "unique_icustay_id                0.000000\n",
            "admission_age                    0.000000\n",
            "sex_female                       0.000000\n",
            "race_ethnicity                   0.000000\n",
            "BMI_admission                    3.793858\n",
            "weight_admission                 2.151699\n",
            "height_admission                 2.353853\n",
            "sofa_past_overall_24hr          22.655147\n",
            "sofa_future_overall_24hr        19.248982\n",
            "vitals_heart_rate               11.157265\n",
            "vitals_mbp_ni                   30.990557\n",
            "vitals_resp_rate                13.998505\n",
            "vitals_tempc                    15.989588\n",
            "cbc_wbc                         15.997895\n",
            "cbc_hematocrit                  13.882197\n",
            "cbc_platelet                    15.228047\n",
            "coag_fibrinogen                 84.245798\n",
            "coag_inr                        35.105646\n",
            "bmp_sodium                      13.472349\n",
            "bmp_potassium                   12.242807\n",
            "bmp_chloride                    16.427127\n",
            "bmp_bicarbonate                 21.403450\n",
            "bmp_bun                         16.114203\n",
            "bmp_creatinine                  16.211127\n",
            "bmp_glucose                      9.520645\n",
            "bmp_aniongap                    28.991166\n",
            "bmp_calcium                     22.450223\n",
            "bmp_lactate                     53.532165\n",
            "hfp_alt                         36.182881\n",
            "hfp_alp                         36.675805\n",
            "hfp_ast                         35.900418\n",
            "hfp_bilirubin_total             39.381352\n",
            "hfp_albumin                     36.301958\n",
            "others_ck_cpk                   74.957769\n",
            "others_ld_ldh                   91.703359\n",
            "in_hospital_mortality            0.814156\n",
            "pH                               0.000000\n",
            "SpO2                             0.000000\n",
            "SaO2                             0.000000\n",
            "sex                              0.000000\n",
            "age_group                        0.000000\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Calculate the percentage of missing values per column\n",
        "missing_data_percentage = data_train.isnull().mean() * 100\n",
        "print(\"Percentage of missing values per column:\\n\", missing_data_percentage)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: What additional insights can be gained by analyzing missing values beyond just the overall percentage per column?\n",
        "\n",
        "- Your Answer: ..."
      ],
      "metadata": {
        "id": "A4-vjIUWjCs-"
      },
      "id": "A4-vjIUWjCs-"
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "fc9c4ad0-fe94-46e3-9bc9-7abca3c4029c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fc9c4ad0-fe94-46e3-9bc9-7abca3c4029c",
        "outputId": "0e0f0ac2-5af9-40f4-947b-0870682155d5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<pandas.io.formats.style.Styler at 0x7c614631a9b0>"
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              "  background-color: #3d0965;\n",
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              "  background-color: #721a6e;\n",
              "  color: #f1f1f1;\n",
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              "#T_36283_row14_col2 {\n",
              "  background-color: #cb4149;\n",
              "  color: #f1f1f1;\n",
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              "#T_36283_row14_col3, #T_36283_row25_col2 {\n",
              "  background-color: #5f136e;\n",
              "  color: #f1f1f1;\n",
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              "#T_36283_row15_col0, #T_36283_row18_col1 {\n",
              "  background-color: #280b53;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row15_col1 {\n",
              "  background-color: #380962;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row15_col2 {\n",
              "  background-color: #a02a63;\n",
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              "  background-color: #260c51;\n",
              "  color: #f1f1f1;\n",
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              "  background-color: #340a5f;\n",
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              "#T_36283_row16_col2 {\n",
              "  background-color: #8f2469;\n",
              "  color: #f1f1f1;\n",
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              "#T_36283_row16_col3 {\n",
              "  background-color: #320a5e;\n",
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              "#T_36283_row17_col0, #T_36283_row26_col0, #T_36283_row27_col0, #T_36283_row29_col2, #T_36283_row29_col3 {\n",
              "  background-color: #390963;\n",
              "  color: #f1f1f1;\n",
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              "#T_36283_row17_col1, #T_36283_row24_col1 {\n",
              "  background-color: #2f0a5b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row17_col2, #T_36283_row19_col2, #T_36283_row25_col3 {\n",
              "  background-color: #210c4a;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row17_col3, #T_36283_row18_col3, #T_36283_row19_col3 {\n",
              "  background-color: #1b0c41;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row18_col0, #T_36283_row19_col1 {\n",
              "  background-color: #2d0b59;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row18_col2, #T_36283_row23_col1, #T_36283_row23_col3, #T_36283_row24_col3, #T_36283_row27_col3 {\n",
              "  background-color: #1e0c45;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row20_col0 {\n",
              "  background-color: #f4df53;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_36283_row20_col1 {\n",
              "  background-color: #f1ef75;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_36283_row20_col2, #T_36283_row20_col3 {\n",
              "  background-color: #f4e156;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_36283_row21_col0 {\n",
              "  background-color: #8d2369;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row21_col1 {\n",
              "  background-color: #9b2964;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row21_col2 {\n",
              "  background-color: #64156e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row21_col3 {\n",
              "  background-color: #67166e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row22_col0 {\n",
              "  background-color: #2b0b57;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row22_col1, #T_36283_row22_col2, #T_36283_row22_col3 {\n",
              "  background-color: #1f0c48;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row23_col2 {\n",
              "  background-color: #180c3c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row24_col2 {\n",
              "  background-color: #240c4f;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row25_col0 {\n",
              "  background-color: #510e6c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row25_col1 {\n",
              "  background-color: #520e6d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row26_col1, #T_36283_row27_col1 {\n",
              "  background-color: #310a5c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row26_col2, #T_36283_row27_col2 {\n",
              "  background-color: #230c4c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row28_col0, #T_36283_row28_col2 {\n",
              "  background-color: #190c3e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row28_col1 {\n",
              "  background-color: #110a30;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row28_col3 {\n",
              "  background-color: #0e092b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row29_col0 {\n",
              "  background-color: #751b6e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row29_col1 {\n",
              "  background-color: #741a6e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row30_col2 {\n",
              "  background-color: #420a68;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row30_col3 {\n",
              "  background-color: #4c0c6b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row31_col0 {\n",
              "  background-color: #d94d3d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row31_col1 {\n",
              "  background-color: #c83f4b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row31_col2 {\n",
              "  background-color: #ca404a;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row31_col3 {\n",
              "  background-color: #e9612b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row32_col0, #T_36283_row35_col1, #T_36283_row35_col3 {\n",
              "  background-color: #932667;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row32_col1 {\n",
              "  background-color: #85216b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row32_col3 {\n",
              "  background-color: #8a226a;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row33_col0 {\n",
              "  background-color: #972766;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row33_col1, #T_36283_row36_col3 {\n",
              "  background-color: #87216b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row33_col3, #T_36283_row34_col3 {\n",
              "  background-color: #88226a;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row34_col0 {\n",
              "  background-color: #922568;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row34_col1 {\n",
              "  background-color: #84206b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row34_col2 {\n",
              "  background-color: #781c6d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row35_col0 {\n",
              "  background-color: #a22b62;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row36_col0 {\n",
              "  background-color: #952667;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row36_col1 {\n",
              "  background-color: #801f6c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row36_col2 {\n",
              "  background-color: #7d1e6d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row37_col0 {\n",
              "  background-color: #fcae12;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_36283_row37_col1 {\n",
              "  background-color: #fca50a;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_36283_row37_col2 {\n",
              "  background-color: #fa9207;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_36283_row37_col3 {\n",
              "  background-color: #fbb61a;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_36283_row38_col0, #T_36283_row38_col1, #T_36283_row38_col2, #T_36283_row38_col3 {\n",
              "  background-color: #fcffa4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_36283_row39_col0, #T_36283_row39_col1 {\n",
              "  background-color: #010106;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_36283_row39_col3 {\n",
              "  background-color: #010005;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_36283\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_36283_level0_col0\" class=\"col_heading level0 col0\" >White</th>\n",
              "      <th id=\"T_36283_level0_col1\" class=\"col_heading level0 col1\" >Black</th>\n",
              "      <th id=\"T_36283_level0_col2\" class=\"col_heading level0 col2\" >Asian</th>\n",
              "      <th id=\"T_36283_level0_col3\" class=\"col_heading level0 col3\" >Hispanic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row0\" class=\"row_heading level0 row0\" >source_db</th>\n",
              "      <td id=\"T_36283_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row0_col1\" class=\"data row0 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row0_col2\" class=\"data row0 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row0_col3\" class=\"data row0 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row1\" class=\"row_heading level0 row1\" >comorbidity_score_value</th>\n",
              "      <td id=\"T_36283_row1_col0\" class=\"data row1 col0\" >0.006697</td>\n",
              "      <td id=\"T_36283_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row2\" class=\"row_heading level0 row2\" >unique_subject_id</th>\n",
              "      <td id=\"T_36283_row2_col0\" class=\"data row2 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row2_col1\" class=\"data row2 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row3\" class=\"row_heading level0 row3\" >unique_hospital_admission_id</th>\n",
              "      <td id=\"T_36283_row3_col0\" class=\"data row3 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row3_col1\" class=\"data row3 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row4\" class=\"row_heading level0 row4\" >unique_icustay_id</th>\n",
              "      <td id=\"T_36283_row4_col0\" class=\"data row4 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row4_col1\" class=\"data row4 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row5\" class=\"row_heading level0 row5\" >admission_age</th>\n",
              "      <td id=\"T_36283_row5_col0\" class=\"data row5 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row5_col1\" class=\"data row5 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row6\" class=\"row_heading level0 row6\" >sex_female</th>\n",
              "      <td id=\"T_36283_row6_col0\" class=\"data row6 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row6_col1\" class=\"data row6 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row6_col2\" class=\"data row6 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row6_col3\" class=\"data row6 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row7\" class=\"row_heading level0 row7\" >race_ethnicity</th>\n",
              "      <td id=\"T_36283_row7_col0\" class=\"data row7 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row7_col1\" class=\"data row7 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row8\" class=\"row_heading level0 row8\" >BMI_admission</th>\n",
              "      <td id=\"T_36283_row8_col0\" class=\"data row8 col0\" >3.619985</td>\n",
              "      <td id=\"T_36283_row8_col1\" class=\"data row8 col1\" >3.652850</td>\n",
              "      <td id=\"T_36283_row8_col2\" class=\"data row8 col2\" >3.857143</td>\n",
              "      <td id=\"T_36283_row8_col3\" class=\"data row8 col3\" >7.164002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row9\" class=\"row_heading level0 row9\" >weight_admission</th>\n",
              "      <td id=\"T_36283_row9_col0\" class=\"data row9 col0\" >1.975755</td>\n",
              "      <td id=\"T_36283_row9_col1\" class=\"data row9 col1\" >1.839378</td>\n",
              "      <td id=\"T_36283_row9_col2\" class=\"data row9 col2\" >1.571429</td>\n",
              "      <td id=\"T_36283_row9_col3\" class=\"data row9 col3\" >6.216696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row10\" class=\"row_heading level0 row10\" >height_admission</th>\n",
              "      <td id=\"T_36283_row10_col0\" class=\"data row10 col0\" >2.387650</td>\n",
              "      <td id=\"T_36283_row10_col1\" class=\"data row10 col1\" >2.538860</td>\n",
              "      <td id=\"T_36283_row10_col2\" class=\"data row10 col2\" >2.857143</td>\n",
              "      <td id=\"T_36283_row10_col3\" class=\"data row10 col3\" >1.124926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row11\" class=\"row_heading level0 row11\" >sofa_past_overall_24hr</th>\n",
              "      <td id=\"T_36283_row11_col0\" class=\"data row11 col0\" >23.112986</td>\n",
              "      <td id=\"T_36283_row11_col1\" class=\"data row11 col1\" >19.352332</td>\n",
              "      <td id=\"T_36283_row11_col2\" class=\"data row11 col2\" >22.142857</td>\n",
              "      <td id=\"T_36283_row11_col3\" class=\"data row11 col3\" >22.320900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row12\" class=\"row_heading level0 row12\" >sofa_future_overall_24hr</th>\n",
              "      <td id=\"T_36283_row12_col0\" class=\"data row12 col0\" >19.345657</td>\n",
              "      <td id=\"T_36283_row12_col1\" class=\"data row12 col1\" >17.512953</td>\n",
              "      <td id=\"T_36283_row12_col2\" class=\"data row12 col2\" >16.571429</td>\n",
              "      <td id=\"T_36283_row12_col3\" class=\"data row12 col3\" >22.616933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row13\" class=\"row_heading level0 row13\" >vitals_heart_rate</th>\n",
              "      <td id=\"T_36283_row13_col0\" class=\"data row13 col0\" >10.454759</td>\n",
              "      <td id=\"T_36283_row13_col1\" class=\"data row13 col1\" >13.704663</td>\n",
              "      <td id=\"T_36283_row13_col2\" class=\"data row13 col2\" >31.714286</td>\n",
              "      <td id=\"T_36283_row13_col3\" class=\"data row13 col3\" >9.236234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row14\" class=\"row_heading level0 row14\" >vitals_mbp_ni</th>\n",
              "      <td id=\"T_36283_row14_col0\" class=\"data row14 col0\" >31.136562</td>\n",
              "      <td id=\"T_36283_row14_col1\" class=\"data row14 col1\" >29.300518</td>\n",
              "      <td id=\"T_36283_row14_col2\" class=\"data row14 col2\" >48.571429</td>\n",
              "      <td id=\"T_36283_row14_col3\" class=\"data row14 col3\" >24.985198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row15\" class=\"row_heading level0 row15\" >vitals_resp_rate</th>\n",
              "      <td id=\"T_36283_row15_col0\" class=\"data row15 col0\" >13.173933</td>\n",
              "      <td id=\"T_36283_row15_col1\" class=\"data row15 col1\" >16.580311</td>\n",
              "      <td id=\"T_36283_row15_col2\" class=\"data row15 col2\" >38.428571</td>\n",
              "      <td id=\"T_36283_row15_col3\" class=\"data row15 col3\" >12.551806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row16\" class=\"row_heading level0 row16\" >vitals_tempc</th>\n",
              "      <td id=\"T_36283_row16_col0\" class=\"data row16 col0\" >15.447726</td>\n",
              "      <td id=\"T_36283_row16_col1\" class=\"data row16 col1\" >17.176166</td>\n",
              "      <td id=\"T_36283_row16_col2\" class=\"data row16 col2\" >34.428571</td>\n",
              "      <td id=\"T_36283_row16_col3\" class=\"data row16 col3\" >15.216104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row17\" class=\"row_heading level0 row17\" >cbc_wbc</th>\n",
              "      <td id=\"T_36283_row17_col0\" class=\"data row17 col0\" >16.603041</td>\n",
              "      <td id=\"T_36283_row17_col1\" class=\"data row17 col1\" >14.611399</td>\n",
              "      <td id=\"T_36283_row17_col2\" class=\"data row17 col2\" >11.428571</td>\n",
              "      <td id=\"T_36283_row17_col3\" class=\"data row17 col3\" >10.361160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row18\" class=\"row_heading level0 row18\" >cbc_hematocrit</th>\n",
              "      <td id=\"T_36283_row18_col0\" class=\"data row18 col0\" >14.252227</td>\n",
              "      <td id=\"T_36283_row18_col1\" class=\"data row18 col1\" >13.290155</td>\n",
              "      <td id=\"T_36283_row18_col2\" class=\"data row18 col2\" >10.571429</td>\n",
              "      <td id=\"T_36283_row18_col3\" class=\"data row18 col3\" >10.065127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row19\" class=\"row_heading level0 row19\" >cbc_platelet</th>\n",
              "      <td id=\"T_36283_row19_col0\" class=\"data row19 col0\" >15.725671</td>\n",
              "      <td id=\"T_36283_row19_col1\" class=\"data row19 col1\" >14.352332</td>\n",
              "      <td id=\"T_36283_row19_col2\" class=\"data row19 col2\" >11.285714</td>\n",
              "      <td id=\"T_36283_row19_col3\" class=\"data row19 col3\" >10.065127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row20\" class=\"row_heading level0 row20\" >coag_fibrinogen</th>\n",
              "      <td id=\"T_36283_row20_col0\" class=\"data row20 col0\" >83.855736</td>\n",
              "      <td id=\"T_36283_row20_col1\" class=\"data row20 col1\" >87.746114</td>\n",
              "      <td id=\"T_36283_row20_col2\" class=\"data row20 col2\" >81.714286</td>\n",
              "      <td id=\"T_36283_row20_col3\" class=\"data row20 col3\" >84.191829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row21\" class=\"row_heading level0 row21\" >coag_inr</th>\n",
              "      <td id=\"T_36283_row21_col0\" class=\"data row21 col0\" >35.339227</td>\n",
              "      <td id=\"T_36283_row21_col1\" class=\"data row21 col1\" >38.834197</td>\n",
              "      <td id=\"T_36283_row21_col2\" class=\"data row21 col2\" >25.000000</td>\n",
              "      <td id=\"T_36283_row21_col3\" class=\"data row21 col3\" >26.642984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row22\" class=\"row_heading level0 row22\" >bmp_sodium</th>\n",
              "      <td id=\"T_36283_row22_col0\" class=\"data row22 col0\" >13.903958</td>\n",
              "      <td id=\"T_36283_row22_col1\" class=\"data row22 col1\" >11.528497</td>\n",
              "      <td id=\"T_36283_row22_col2\" class=\"data row22 col2\" >10.857143</td>\n",
              "      <td id=\"T_36283_row22_col3\" class=\"data row22 col3\" >11.367673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row23\" class=\"row_heading level0 row23\" >bmp_potassium</th>\n",
              "      <td id=\"T_36283_row23_col0\" class=\"data row23 col0\" >12.567812</td>\n",
              "      <td id=\"T_36283_row23_col1\" class=\"data row23 col1\" >10.906736</td>\n",
              "      <td id=\"T_36283_row23_col2\" class=\"data row23 col2\" >9.285714</td>\n",
              "      <td id=\"T_36283_row23_col3\" class=\"data row23 col3\" >10.775607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row24\" class=\"row_heading level0 row24\" >bmp_chloride</th>\n",
              "      <td id=\"T_36283_row24_col0\" class=\"data row24 col0\" >17.095305</td>\n",
              "      <td id=\"T_36283_row24_col1\" class=\"data row24 col1\" >14.481865</td>\n",
              "      <td id=\"T_36283_row24_col2\" class=\"data row24 col2\" >12.142857</td>\n",
              "      <td id=\"T_36283_row24_col3\" class=\"data row24 col3\" >10.834813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row25\" class=\"row_heading level0 row25\" >bmp_bicarbonate</th>\n",
              "      <td id=\"T_36283_row25_col0\" class=\"data row25 col0\" >21.817025</td>\n",
              "      <td id=\"T_36283_row25_col1\" class=\"data row25 col1\" >22.046632</td>\n",
              "      <td id=\"T_36283_row25_col2\" class=\"data row25 col2\" >24.000000</td>\n",
              "      <td id=\"T_36283_row25_col3\" class=\"data row25 col3\" >11.545293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row26\" class=\"row_heading level0 row26\" >bmp_bun</th>\n",
              "      <td id=\"T_36283_row26_col0\" class=\"data row26 col0\" >16.700154</td>\n",
              "      <td id=\"T_36283_row26_col1\" class=\"data row26 col1\" >14.818653</td>\n",
              "      <td id=\"T_36283_row26_col2\" class=\"data row26 col2\" >11.571429</td>\n",
              "      <td id=\"T_36283_row26_col3\" class=\"data row26 col3\" >10.597987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row27\" class=\"row_heading level0 row27\" >bmp_creatinine</th>\n",
              "      <td id=\"T_36283_row27_col0\" class=\"data row27 col0\" >16.780524</td>\n",
              "      <td id=\"T_36283_row27_col1\" class=\"data row27 col1\" >15.000000</td>\n",
              "      <td id=\"T_36283_row27_col2\" class=\"data row27 col2\" >11.714286</td>\n",
              "      <td id=\"T_36283_row27_col3\" class=\"data row27 col3\" >10.775607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row28\" class=\"row_heading level0 row28\" >bmp_glucose</th>\n",
              "      <td id=\"T_36283_row28_col0\" class=\"data row28 col0\" >9.892171</td>\n",
              "      <td id=\"T_36283_row28_col1\" class=\"data row28 col1\" >7.746114</td>\n",
              "      <td id=\"T_36283_row28_col2\" class=\"data row28 col2\" >9.428571</td>\n",
              "      <td id=\"T_36283_row28_col3\" class=\"data row28 col3\" >7.045589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row29\" class=\"row_heading level0 row29\" >bmp_aniongap</th>\n",
              "      <td id=\"T_36283_row29_col0\" class=\"data row29 col0\" >29.867390</td>\n",
              "      <td id=\"T_36283_row29_col1\" class=\"data row29 col1\" >29.948187</td>\n",
              "      <td id=\"T_36283_row29_col2\" class=\"data row29 col2\" >16.285714</td>\n",
              "      <td id=\"T_36283_row29_col3\" class=\"data row29 col3\" >16.577857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row30\" class=\"row_heading level0 row30\" >bmp_calcium</th>\n",
              "      <td id=\"T_36283_row30_col0\" class=\"data row30 col0\" >23.019222</td>\n",
              "      <td id=\"T_36283_row30_col1\" class=\"data row30 col1\" >19.663212</td>\n",
              "      <td id=\"T_36283_row30_col2\" class=\"data row30 col2\" >17.857143</td>\n",
              "      <td id=\"T_36283_row30_col3\" class=\"data row30 col3\" >20.663114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row31\" class=\"row_heading level0 row31\" >bmp_lactate</th>\n",
              "      <td id=\"T_36283_row31_col0\" class=\"data row31 col0\" >53.834304</td>\n",
              "      <td id=\"T_36283_row31_col1\" class=\"data row31 col1\" >49.740933</td>\n",
              "      <td id=\"T_36283_row31_col2\" class=\"data row31 col2\" >48.142857</td>\n",
              "      <td id=\"T_36283_row31_col3\" class=\"data row31 col3\" >59.088218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row32\" class=\"row_heading level0 row32\" >hfp_alt</th>\n",
              "      <td id=\"T_36283_row32_col0\" class=\"data row32 col0\" >36.745697</td>\n",
              "      <td id=\"T_36283_row32_col1\" class=\"data row32 col1\" >33.730570</td>\n",
              "      <td id=\"T_36283_row32_col2\" class=\"data row32 col2\" >30.000000</td>\n",
              "      <td id=\"T_36283_row32_col3\" class=\"data row32 col3\" >34.399053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row33\" class=\"row_heading level0 row33\" >hfp_alp</th>\n",
              "      <td id=\"T_36283_row33_col0\" class=\"data row33 col0\" >37.308285</td>\n",
              "      <td id=\"T_36283_row33_col1\" class=\"data row33 col1\" >34.067358</td>\n",
              "      <td id=\"T_36283_row33_col2\" class=\"data row33 col2\" >29.857143</td>\n",
              "      <td id=\"T_36283_row33_col3\" class=\"data row33 col3\" >34.280639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row34\" class=\"row_heading level0 row34\" >hfp_ast</th>\n",
              "      <td id=\"T_36283_row34_col0\" class=\"data row34 col0\" >36.471100</td>\n",
              "      <td id=\"T_36283_row34_col1\" class=\"data row34 col1\" >33.419689</td>\n",
              "      <td id=\"T_36283_row34_col2\" class=\"data row34 col2\" >29.714286</td>\n",
              "      <td id=\"T_36283_row34_col3\" class=\"data row34 col3\" >34.043813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row35\" class=\"row_heading level0 row35\" >hfp_bilirubin_total</th>\n",
              "      <td id=\"T_36283_row35_col0\" class=\"data row35 col0\" >40.044203</td>\n",
              "      <td id=\"T_36283_row35_col1\" class=\"data row35 col1\" >37.046632</td>\n",
              "      <td id=\"T_36283_row35_col2\" class=\"data row35 col2\" >30.142857</td>\n",
              "      <td id=\"T_36283_row35_col3\" class=\"data row35 col3\" >36.826525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row36\" class=\"row_heading level0 row36\" >hfp_albumin</th>\n",
              "      <td id=\"T_36283_row36_col0\" class=\"data row36 col0\" >37.037037</td>\n",
              "      <td id=\"T_36283_row36_col1\" class=\"data row36 col1\" >32.797927</td>\n",
              "      <td id=\"T_36283_row36_col2\" class=\"data row36 col2\" >30.571429</td>\n",
              "      <td id=\"T_36283_row36_col3\" class=\"data row36 col3\" >33.688573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row37\" class=\"row_heading level0 row37\" >others_ck_cpk</th>\n",
              "      <td id=\"T_36283_row37_col0\" class=\"data row37 col0\" >75.195901</td>\n",
              "      <td id=\"T_36283_row37_col1\" class=\"data row37 col1\" >73.834197</td>\n",
              "      <td id=\"T_36283_row37_col2\" class=\"data row37 col2\" >67.428571</td>\n",
              "      <td id=\"T_36283_row37_col3\" class=\"data row37 col3\" >76.435761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row38\" class=\"row_heading level0 row38\" >others_ld_ldh</th>\n",
              "      <td id=\"T_36283_row38_col0\" class=\"data row38 col0\" >91.685085</td>\n",
              "      <td id=\"T_36283_row38_col1\" class=\"data row38 col1\" >92.409326</td>\n",
              "      <td id=\"T_36283_row38_col2\" class=\"data row38 col2\" >88.857143</td>\n",
              "      <td id=\"T_36283_row38_col3\" class=\"data row38 col3\" >91.592658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row39\" class=\"row_heading level0 row39\" >in_hospital_mortality</th>\n",
              "      <td id=\"T_36283_row39_col0\" class=\"data row39 col0\" >0.807046</td>\n",
              "      <td id=\"T_36283_row39_col1\" class=\"data row39 col1\" >0.777202</td>\n",
              "      <td id=\"T_36283_row39_col2\" class=\"data row39 col2\" >2.142857</td>\n",
              "      <td id=\"T_36283_row39_col3\" class=\"data row39 col3\" >0.473653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row40\" class=\"row_heading level0 row40\" >pH</th>\n",
              "      <td id=\"T_36283_row40_col0\" class=\"data row40 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row40_col1\" class=\"data row40 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row40_col2\" class=\"data row40 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row40_col3\" class=\"data row40 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row41\" class=\"row_heading level0 row41\" >SpO2</th>\n",
              "      <td id=\"T_36283_row41_col0\" class=\"data row41 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row41_col1\" class=\"data row41 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row41_col2\" class=\"data row41 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row41_col3\" class=\"data row41 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row42\" class=\"row_heading level0 row42\" >SaO2</th>\n",
              "      <td id=\"T_36283_row42_col0\" class=\"data row42 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row42_col1\" class=\"data row42 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row42_col2\" class=\"data row42 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row42_col3\" class=\"data row42 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row43\" class=\"row_heading level0 row43\" >sex</th>\n",
              "      <td id=\"T_36283_row43_col0\" class=\"data row43 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row43_col1\" class=\"data row43 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row43_col2\" class=\"data row43 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row43_col3\" class=\"data row43 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_36283_level0_row44\" class=\"row_heading level0 row44\" >age_group</th>\n",
              "      <td id=\"T_36283_row44_col0\" class=\"data row44 col0\" >0.000000</td>\n",
              "      <td id=\"T_36283_row44_col1\" class=\"data row44 col1\" >0.000000</td>\n",
              "      <td id=\"T_36283_row44_col2\" class=\"data row44 col2\" >0.000000</td>\n",
              "      <td id=\"T_36283_row44_col3\" class=\"data row44 col3\" >0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Calculate percentage of missing values per column and per race\n",
        "# Please add your code here ...\n",
        "missing_data_percentage_w = data_train[data_train[\"race_ethnicity\"]==\"White\"].isnull().mean() * 100\n",
        "missing_data_percentage_b = data_train[data_train[\"race_ethnicity\"]==\"Black\"].isnull().mean() * 100\n",
        "missing_data_percentage_a = data_train[data_train[\"race_ethnicity\"]==\"Asian\"].isnull().mean() * 100\n",
        "missing_data_percentage_h = data_train[data_train[\"race_ethnicity\"]== \"Hispanic OR Latino\"].isnull().mean() * 100\n",
        "miss_race_percentage = pd.DataFrame({\"White\": missing_data_percentage_w, \"Black\": missing_data_percentage_b, \"Asian\": missing_data_percentage_a, \"Hispanic\": missing_data_percentage_h})\n",
        "\n",
        "display(miss_race_percentage.style.background_gradient(cmap='inferno'))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: What observations can you make? Are there any columns that we should consider dropping?\n",
        "\n",
        "- Your answer: ...."
      ],
      "metadata": {
        "id": "hb2r2qXMpK2J"
      },
      "id": "hb2r2qXMpK2J"
    },
    {
      "cell_type": "code",
      "source": [
        "# consider dropping 'coag_fibrinogen', \"others_ck_cpk\", \"others_ld_ldh\", \"bmp_lactate\"\n",
        "# only \"others_ck_cpk\" has no statistical significance\n",
        "\n",
        "data_train_dropped = data_train.drop([\"others_ck_cpk\"], axis=1)\n",
        "\n",
        "# Check if the columns were dropped\n",
        "print(data_train_dropped.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5YKo99cntxzo",
        "outputId": "85f5fd35-f8e0-473c-9605-6bbb4645e01d"
      },
      "id": "5YKo99cntxzo",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['source_db', 'comorbidity_score_value', 'unique_subject_id',\n",
            "       'unique_hospital_admission_id', 'unique_icustay_id', 'admission_age',\n",
            "       'sex_female', 'race_ethnicity', 'BMI_admission', 'weight_admission',\n",
            "       'height_admission', 'sofa_past_overall_24hr',\n",
            "       'sofa_future_overall_24hr', 'vitals_heart_rate', 'vitals_mbp_ni',\n",
            "       'vitals_resp_rate', 'vitals_tempc', 'cbc_wbc', 'cbc_hematocrit',\n",
            "       'cbc_platelet', 'coag_fibrinogen', 'coag_inr', 'bmp_sodium',\n",
            "       'bmp_potassium', 'bmp_chloride', 'bmp_bicarbonate', 'bmp_bun',\n",
            "       'bmp_creatinine', 'bmp_glucose', 'bmp_aniongap', 'bmp_calcium',\n",
            "       'bmp_lactate', 'hfp_alt', 'hfp_alp', 'hfp_ast', 'hfp_bilirubin_total',\n",
            "       'hfp_albumin', 'others_ld_ldh', 'in_hospital_mortality', 'pH', 'SpO2',\n",
            "       'SaO2', 'sex', 'age_group'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Please add your code here ...\n"
      ],
      "metadata": {
        "id": "DV3gdEMBkNVw"
      },
      "id": "DV3gdEMBkNVw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: How do you think we should handle missing values? How should we handle missing values for the test set specifically?\n",
        "\n",
        "- Your answer: ....\n"
      ],
      "metadata": {
        "id": "1D11rKADleTO"
      },
      "id": "1D11rKADleTO"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2975459d-ebbe-4680-acc1-75020641c981",
      "metadata": {
        "id": "2975459d-ebbe-4680-acc1-75020641c981"
      },
      "outputs": [],
      "source": [
        "def impute_by_race(df, continuous_vars, categorical_vars):\n",
        "\n",
        "    # Please add your code here ...\n",
        "    # Initialize an empty dictionary to store the imputed values\n",
        "    impute_values = {\n",
        "        'continuous': {},\n",
        "        'categorical': {}\n",
        "    }\n",
        "    for var in continuous_vars:\n",
        "        # Calculate the median value for each race/ethnicity group\n",
        "        median_values = df.groupby('race_ethnicity')[var].median()\n",
        "        # Store the imputed median values in the dictionary\n",
        "        impute_values['continuous'][var] = median_values\n",
        "\n",
        "    for var in categorical_vars:\n",
        "        # Calculate the mode value for each race/ethnicity group\n",
        "        mode_values = df.groupby('race_ethnicity')[var].agg(pd.Series.mode)\n",
        "        # Store the imputed mode values in the dictionary\n",
        "        impute_values['categorical'][var] = mode_values\n",
        "\n",
        "\n",
        "    # Return the dataframe with imputed values and the dictionary of imputed values\n",
        "    return df, impute_values\n",
        "\n",
        "def apply_imputation_for_test_set(test_df, impute_values, continuous_vars, categorical_vars):\n",
        "    # Loop through each continuous variable to apply the stored imputed values to the test set\n",
        "    for var in continuous_vars:\n",
        "        # Loop through each race/ethnicity to apply the respective imputed values\n",
        "        for race in impute_values['continuous'][var].index:\n",
        "            # Apply the imputed median value to the missing values in the test set for the respective race/ethnicity group\n",
        "            test_df.loc[test_df['race_ethnicity'] == race, var] = test_df.loc[test_df['race_ethnicity'] == race, var].fillna(impute_values['continuous'][var][race])\n",
        "\n",
        "    # Loop through each categorical variable to apply the stored imputed values to the test set\n",
        "    for var in categorical_vars:\n",
        "        # Loop through each race/ethnicity to apply the respective imputed values\n",
        "        for race in impute_values['categorical'][var].index:\n",
        "            # Apply the imputed mode value to the missing values in the test set for the respective race/ethnicity group\n",
        "            test_df.loc[test_df['race_ethnicity'] == race, var] = test_df.loc[test_df['race_ethnicity'] == race, var].fillna(impute_values['categorical'][var][race])\n",
        "\n",
        "    # Return the test dataframe with imputed values\n",
        "    return test_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9499a04f-14d6-469e-bc80-0c5f06e465dd",
      "metadata": {
        "id": "9499a04f-14d6-469e-bc80-0c5f06e465dd"
      },
      "outputs": [],
      "source": [
        "# Define continuous and categorical variables\n",
        "\n",
        "categorical_vars = [\n",
        "    'source_db', 'unique_subject_id', 'unique_hospital_admission_id', 'unique_icustay_id',\n",
        "    'sex_female', 'race_ethnicity', 'in_hospital_mortality'\n",
        "]\n",
        "\n",
        "continuous_vars = [\n",
        "    'comorbidity_score_value', 'admission_age', 'BMI_admission', 'weight_admission',\n",
        "    'height_admission', 'sofa_past_overall_24hr', 'sofa_future_overall_24hr',\n",
        "    'vitals_heart_rate', 'vitals_mbp_ni', 'vitals_resp_rate', 'vitals_tempc',\n",
        "    'cbc_wbc', 'cbc_hematocrit', 'cbc_platelet', 'coag_inr',\n",
        "    'bmp_sodium', 'bmp_potassium', 'bmp_chloride', 'bmp_bicarbonate', 'bmp_bun', 'bmp_creatinine',\n",
        "    'bmp_glucose', 'bmp_aniongap', 'bmp_calcium', 'bmp_lactate', 'hfp_alt', 'hfp_alp', 'hfp_ast',\n",
        "    'hfp_bilirubin_total', 'hfp_albumin', 'others_ck_cpk',\n",
        "    'SpO2', 'SaO2', 'pH'\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "01dd3c4d-1400-4761-9d30-db2891d4663b",
      "metadata": {
        "id": "01dd3c4d-1400-4761-9d30-db2891d4663b"
      },
      "outputs": [],
      "source": [
        "data_train_imputed, impute_values = impute_by_race(data_train, continuous_vars, categorical_vars)\n",
        "data_test_imputed = apply_imputation_for_test_set(data_test, impute_values, continuous_vars, categorical_vars)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "11fe801a-823e-4744-80ea-eb260ed3d96b",
      "metadata": {
        "id": "11fe801a-823e-4744-80ea-eb260ed3d96b"
      },
      "outputs": [],
      "source": [
        "# Remove rows where target variable 'in_hospital_mortality' is missing\n",
        "data_train_imputed_final = data_train_imputed.dropna(subset=['in_hospital_mortality'])\n",
        "data_test_imputed_final = data_test_imputed.dropna(subset=['in_hospital_mortality'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e2b6c41b-e454-463f-ac0c-87fcbabd05fe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2b6c41b-e454-463f-ac0c-87fcbabd05fe",
        "outputId": "b7b561be-9a9a-433a-fc41-e852827da336"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are no missing values in the training set.\n",
            "There are no missing values in the test set.\n"
          ]
        }
      ],
      "source": [
        "# Check for remaining missing values in the imputed training set\n",
        "missing_data_train = data_train_imputed_final.isnull().sum()\n",
        "missing_columns_train = missing_data_train[missing_data_train > 0]\n",
        "\n",
        "if missing_columns_train.empty:\n",
        "    print(\"There are no missing values in the training set.\")\n",
        "else:\n",
        "    print(\"Columns with remaining missing values in the training set:\\n\", missing_columns_train)\n",
        "\n",
        "# Check for remaining missing values in the imputed test set\n",
        "missing_data_test = data_test_imputed_final.isnull().sum()\n",
        "missing_columns_test = missing_data_test[missing_data_test > 0]\n",
        "\n",
        "if missing_columns_test.empty:\n",
        "    print(\"There are no missing values in the test set.\")\n",
        "else:\n",
        "    print(\"Columns with remaining missing values in the test set:\\n\", missing_columns_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optional: Try other imputation methods"
      ],
      "metadata": {
        "id": "yxKB-4GCqgb1"
      },
      "id": "yxKB-4GCqgb1"
    },
    {
      "cell_type": "code",
      "source": [
        "# Your code here...."
      ],
      "metadata": {
        "id": "AD-HSdVGqn2D"
      },
      "id": "AD-HSdVGqn2D",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "ff46297d-e021-4886-b06f-62179ff994d5",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "id": "ff46297d-e021-4886-b06f-62179ff994d5"
      },
      "source": [
        "# *Optional:* Apply you Inclusion / Exclusion Criteria\n",
        "\n",
        "\n",
        "* How to identify Sampling and Representation Bias in a systematic way?\n",
        "    - When we apply inclusion / exclusion criteria, it is important to keep track of the patients that are being left out of the study to spotlight points of sampling bias. We can create a function that gets the demographic information from a dataframe of patients, to be run this each time we apply an exclusion criteria.\n",
        "\n",
        "This is a good practice of study cohort selection!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "51be6529-ba2d-4375-93a2-82f8cde60cb2",
      "metadata": {
        "id": "51be6529-ba2d-4375-93a2-82f8cde60cb2"
      },
      "outputs": [],
      "source": [
        "# Write the code for the inclusion/exclusion criteria here\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ccac99d4",
      "metadata": {
        "id": "ccac99d4"
      },
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a2a4d08-efd5-45de-b6d6-b605e57dce0a",
      "metadata": {
        "id": "5a2a4d08-efd5-45de-b6d6-b605e57dce0a"
      },
      "source": [
        "##  Visualization of Mortality per Ethnic Group"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: How do you suggest we can do so, and why?\n",
        "- Your answer: ....\n"
      ],
      "metadata": {
        "id": "VeMhbEB8uEVg"
      },
      "id": "VeMhbEB8uEVg"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f669bf60-cb1e-42f4-b164-c101f6501eea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f669bf60-cb1e-42f4-b164-c101f6501eea",
        "outputId": "983b94f3-09c5-4cce-9fc3-a3ce65dcdc66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "race_ethnicity\n",
            "Asian                 0.180000\n",
            "Black                 0.171503\n",
            "Hispanic OR Latino    0.182948\n",
            "White                 0.172828\n",
            "Name: in_hospital_mortality, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Please add your code here ...\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4e17d0e8",
      "metadata": {
        "id": "4e17d0e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "278b215e-5b04-4e03-9e17-3df4cfa5a38a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# let's plot the results!\n",
        "\n",
        "# Ensure matplotlib uses an inline backend\n",
        "%matplotlib inline\n",
        "\n",
        "# Create the bar plot with a specified figure size\n",
        "plt.figure (figsize =(10,6))\n",
        "\n",
        "# Plot the mean mortality per ethnic group as a bar plot\n",
        "mortality_per_ethnicty.plot(kind = 'bar', color = 'skyblue')\n",
        "\n",
        "# Add labels and titles to the plot for a better understanding\n",
        "plt.xlabel('Ethnicity') # Label for the x-axis\n",
        "plt.ylabel('Mean Hospital Deaths') # Lable for the y-axis\n",
        "plt.title('Mean Mortality per Ethnic Group') # Title of the plot\n",
        "\n",
        "# Rotate the x-axis labels for better readability\n",
        "plt.xticks(rotation = 45)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d788dc1d",
      "metadata": {
        "id": "d788dc1d"
      },
      "source": [
        "## Initial Analysis of SpO2 and SaO2\n",
        "* Present the statistical summary and visualize the distribution of SpO2 and SaO2 values.\n",
        "* Highlight any differences in SpO2 and SaO2 distributions across different demographic groups (e.g., race_ethnicity, sex).\n",
        "\n",
        "Goal: To explore the distribution of SpO2 and SaO2 values across different demographics. This step helps to identify any preliminary differences or patterns in SpO2 and SaO2 measurements among various groups."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "99931ae2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99931ae2",
        "outputId": "89498d18-1b0f-4ecf-b893-d979854a498d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SpO2 summary:\n",
            "\n",
            "count    36111.000000\n",
            "mean        97.113151\n",
            "std          3.855122\n",
            "min         70.000000\n",
            "25%         96.000000\n",
            "50%         98.000000\n",
            "75%        100.000000\n",
            "max        100.000000\n",
            "\n",
            "\n",
            "SaO2 summary:\n",
            "\n",
            "count    36111.000000\n",
            "mean        95.902639\n",
            "std          4.175827\n",
            "min         70.000000\n",
            "25%         94.400000\n",
            "50%         97.000000\n",
            "75%         99.000000\n",
            "max        100.000000\n"
          ]
        }
      ],
      "source": [
        "# Statistical summary of SpO2 values\n",
        "Sp02_summary = data_train_imputed_final['SpO2'].describe()\n",
        "print(\"SpO2 summary:\\n\")\n",
        "print(Sp02_summary.to_string())\n",
        "print (\"\\n\")\n",
        "\n",
        "SaO2_summary = data_train_imputed_final['SaO2'].describe()\n",
        "print(\"SaO2 summary:\\n\")\n",
        "print(SaO2_summary.to_string())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c407bdda",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "c407bdda",
        "outputId": "ef6cb9a3-9d2d-4e68-a2ad-a4521dfe5c0e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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c3Nzk4+OjoUOHavTo0fLz85O3t7eef/55hYeHq0OHDpKknj17qlmzZnr88cc1adIkpaSk6LXXXlN0dLRlpuXw4cP17rvv6uWXX9aTTz6plStXav78+Vq8eLHNzh0AAFRtqZk5Sko/KweT1Laur63LAQAAqDQMw9CmQ6e0JfN8gBnVsqYmP9Rars6ONq7MNgry85SQkGDVlpCQoKCQRqrfsr2NqrK9Sh1qvv/++5Kkrl27WrXPnj1bTzzxhCRp6tSpcnBwUP/+/ZWbm6vIyEi99957lr6Ojo5atGiRnn32WYWHh8vDw0ODBw/W+PHjLX1CQ0O1ePFijRo1StOnT1ft2rX18ccfKzIy8oafIwAAuDmt+fO4JKlFLR95uTrbuBoAAIDKoajI0Oo/j2v70QxJUu9Qs9555OZ+ynna4QPafSpde/L+d3dPwq+bFNSolRrZsC5bq9ShpmEYV+zj6uqqmTNnaubMmZfsExISop9++umy++natau2bNlyzTUCAACUxeo950PNro0DbFwJAABA5VBgSIu2H1PiiWxJUnPPc3q0mfdNHWgWq1G7vtWszOQDu21YTeVQqUNNAACAqii/sEhr9p4PNbs1rmHjagAAAGwvr9BQ/GkPncjPlqODSb2aB8khleAOl0aoCQAAJEl5eSXX6mndurXMZrONKqq6fj90SmdyCuTnYVar2r62LgcAAMCm8guL9O7v53Qi30nOjib1bVNLwb5uOpBq68puvNKeYp6QkKDCQh8bVWQ/CDUBAICk84OnKfNXKCikoSQp5dA+jZYUFhZm28KqoFX/vfW8yy015MjtVAAA4CZWWGRo1Lyt2pJWIAcZurf1+UDzZlH8FPNa9ZtY2lgv8+oQagIAAIugkIaq27iVrcuo8lbvSZMkdeXWcwAAcBMrKjL01//bpkXbjsnRJLXzPqva1dxtXVaFq1W/CetllgGhJgAAQAVKPn1Ou1POyMEkdW5EqAkAwM2utNuPw8LC5OrqaqOKKoZhGBq/aJcWbD4iB5MUfaubEo9k2Los2BFCTQAAgApU/NTzW+tWUzUP1isFAOBmd/Htx0cP7NYoSZ06dbJtYTfYf5bt0Zz1B8//84OtVSM7UYlHbFsT7AuhJgAAQAVa/sf5Fe956jkAACh28e3HVd3MVfs0c9V+SdKEvi3Ur21trV2baOOqYG8cbF0AAADAzSItM0e//Hl+pmavFkE2rgYAAKDizV6XqLeX7pEk/a1PEz3eIcTGFcFeMVMTAACggizYfESFRYbah1RTwwAvW5cDAABQoT5ee0CvL/5DkvRij0Ya1rmBjSuCPSPUBAAAqABFhqF58YclSQ+H1bFxNQAAABXHMAy9vXSP3lt9/pbzZ7rU18iIRjauCvaOUBMAAKAC7Dyer6T0s/JycVJUq5q2LgcAAKBC5OQX6rWFO/TN5vNPAXqlVxMN71JfJpPJxpXB3hFqAgAAVIAVieckSfe2CZa7mSEYAACo+g6nn9Wzczdrx9FMOZikN+5vqUduq2vrslBFMKIGAAC4wXILpQ2puZLEQB4AANwUlu1M0ZhvtinjXL78PMyaMeBWdWxU3dZloQoh1AQAALjB9mc5qaBIalHLWy1q+di6HAAAgBumwJDmbD+nFUmbJUmta/vovcfaqZavm40rQ1VDqAkAAHADZecWaF/W+SHXiG4siA8AAKqu42dytTbdU1mF+ZKkZzrX1196NpbZycHGlaEqItQEAAC4gTYkpqvQMKmRn5MimwfauhwAAIByZxiGEo5k6Nd9J1RY5CgfF5PefSxMnRrVsHVpN5WC/DwlJCRYtSUkJKiwsGreKUSoCQAAcIOcPpunnckZkqSBLTx5yicAAKhycotM+iEhWQdPnpUkBZrzNbZTNQJNG0g7fEC7T6VrT56fpS3h100KatRKVfF+IUJNAACqgLy8kldlW7duLbPZbKOKIEnr959UkSEFuhaqeQ3+FgAAoGrZdrxAv6R7KrforBwdTOrUsLo8T+2Tt4u/rUu7oXJychQfH2/Vlpt7/qGQLi4ukmw3Q7JG7fqq37K95X3ygd0VXkNFIdQEAKAKSEhI0JT5KxQU0lCSlHJon0ZLCgsLs21hN7Hkcw7aezJLJknNvfNtXQ4AAEC5yS0o1NtL9ujjjWclOcjfw6xeLYJU3dNFB07burobLz4+XlPnxapW/SaWtoRfl8nRzVMt2t3x3/dVd4ZkZUGoCQBAFREU0lB1G7eydRmQlJFTpC2nzs/MbBtSTT6FyTauCAAAoHz8mXpGo+Zt1c7kTElSPbdcRYU1kJPjzfUwoFr1m5SYEeno4Wtpq8ozJCsLQk0AAIByZBiGPvg9U3lFJvl7mtWhvp+S9xJqAgAA+5adW6AZK/bqk18TVVBkqJq7s55o5qR9SRk3XaCJyoFQEwAAoBx9+dshxR/Lk0mGIpsFycmBQT4AAFVNaWsqSueX/nF1dbVBRTdOdm6Bvo4/rI/WHFBKZo4k6a5mgXq9bwv9mRCvfUk2LhA3LUJNAACAcrJ8V6r++cNOSVIznwLV8HKxcUUAAOBGKG1NxaMHdmuUpE6dOtmusHJ0psBBX/+Ro+dXrdTps+fXB6/j56Z/3dtc3ZsESpL+tGWBuOkRagIAAJSDLUmnNOKr31VkSN3ruco7/5ytSwIAADfQxWsqVgVn8wq0J+WMdqecUdoZLyk9T5JUz99dwzo3UL+2teTq7GjjKoHzCDUBAACu07KdKXppQYJy8ovUtXENPd1Mmh9/ytZlAQAAXJFhGPozvUDxGe5K+zVRRcb5dpMMtQ101rCerRXRNFCODibbFgpchFATAACgjM7lFWrS0t2ave6gJKldSDXNfLStdm3bYtvCAAAArqCgsEhLd6bqo7UHtPXwWUnOkqRAbxc1DfKWOX2/Hm9fW52aB9m20Bvo4rVRExISVFjoY8OKcC0INQEAAK5CfmGRTmTlKjUzV0dPndPyP1K1bGeKsvMKJUlPdwrVmMgmMjvxYCAAAFB5ZecWaP6mw/p0XaIOp59fLsfZQappzlPn1g3l73l+TfADpw1bllnuCvLzlJCQYNWWkJCg5buOqU7D5uff/7pJQY1aqVEF10CQWjaEmgAAABcxDCn59DkdPJmtfWkuWrnohDK//VlGKWP72tXcNO6e5opoFljxhQIAgKt2Mz2xvDTHMs7pi7hD+vK3Q8rMKZAkVXN31uMdQnSLQ4qWbjtqCTSrorTDB7T7VLr25PlZ2opDzOK1UZMP7LZZDTcqSK3KCDUBALCBvLySV2lbt24ts9lso4ognb+d/Od9Z7UsxUVnjx75b6uDpCJJkpODSYHerqrh5aLWtX10b5tgta1bTSYTa0wBAFDZ3QxPLL9YTn6hVu85rvmbDmv1njTLepmh1T00tGOo+retLTezo9auTbNtoRWkRu36Vg93utEhZmWtoaog1AQAwAYSEhI0Zf4KBYU0lCSlHNqn0To/UwAVL+Nsvj6PO6jZ6w8qPTtPkoNcnBwU4u8ur7x0Pdw2UN3D28nP3SwHFskHAMBuVcUnll8sp8DQT9uP6aftx7Rqd5plqRxJ6lDfT0PuDOXBP6gSCDUBALCRoJCGqtu4la3LuGnl5BdqQ2K6lu9K1be/H7EM+AM9HFTTOUd3tmkqJ0cHJe05rlBfZ1WvwrdjAQAA+1ZUZOjgyWzFZ7hraewZ5RX9btlW08dV97YJ1oCwugqt7mHDKu0T62BWXoSaAACgyjmbV6ADJ7J1+my+Uk466881p+Sz9TeZTFJWbqFOn81TSkaOcguKLJ9pWtNbz3ZtoICcI5oXnyQnRx74AwAAKre8IpPi9p/UzuSM/16gPf8E87p+7urdIki9WgSpdW1f7jS5DqyDWXkRagIAgCqhsMhQ3JEcrT9uVtrRxAse6uOko+fypeMnS3wmyNtV3ZrUUO8WNdWpUXWZTCbFxx+t0LoBAACuVXp2nubtztHyk14qPJEuSXJzdlRNp7N6sp2fBkZ1Yc3vcsQ6mJUToSYAALBrhUWGFm1L1jsr92lfWpYkR0lSoLeLanq7qfBMmrrdUl0NGtSXJLmbneTrfv528nr+7gz4AQCA3cjJL9Sc9Qc1c9U+nckpkGRSDU8Xta9XTQ1qeOrQzs0K8XGs0uOb0p5if7M8wR7WCDUBAIBdyi8s0vdbkzVz1T4lnsiWJHk4m1TbNU+3t2ioau7nnySftOeYOtV1VVibWrYsFwAAoMyKigz9kJCst5fu0dHT5yRJdb0dFGg6o/B2Dat0iHmxi59iX9WfYI9LI9QEAAB2Ib+wSKmZOdqTckYrdqdpxR+pSs3MlST5ujvr6U711dzlpL7//bAl0AQAAFXPzTRTzzAMrd17QpOW7taOo5mSzj/456WejVU9+4C+2XTqpgo0i90MT7HHlRFqAgCASiWnUDpwPEupmbk6ctKs7SvTlblsuY5n5V6wTuZ5/h5mPd25vh7rECJPFyfFx5+yTdEAAKDC3Awz9YoMQ8t2pmjm6v1KOHxakuTp4qRnuzbQ0I6hcnV21Nq1ibYtsgLw5HFcDqEmAADXKC+v5OCqdevWMpuZHVhWp7LztGhbsr789ZT2nHSTjh377xZH6VyBpAJJktnRQbWqualjw+rq3jRA4fX95ersaLO6AQCAbVTVmXpncvK1J9tFo1dm6WTOZkmSq7ODHrmtrqK7NVR1TxcbV1ixePI4LodQEwCAa5SQkKAp81coKKShJCnl0D6N1vnbnnD1iooMbTyYrpgNSVqyI0V5hUX/3WLI38NFgd6ucjx7Qr2a1lDn9i0V5OMqfw+zHBxuvlusAABA1VVQZOhYrpO2bz2qQyfPypCrJEO+7s4aEFZXT3UKvenCzAvx5HFcCqEmAABlEBTSUHUbt7J1GXYjJ79Qx8/kKu1MjlIycrV+/wnF7kpV2plcS5/mwd5q51+ktBPH1bjZLZKkpD2puq2Wi1rW5hYjAABQtRxOP6uv45M0d32WTud6SDorSfJ3LtBDzb30Yv8uVeaOlJtpHVRUHEJNAABQLnILDZ3IdVDawXSdzM5Tdm6BTp9x0bIfjiv7/5aU+hlPFyfd07qmHr0tRC1r+yg+Pl4xLIsJAECVQ6h1Xl5BkZb/kaqvNiZp7d4TlnazqUgt6/ireS1vndq/TXfU8qsygaZ0Y9dBZd3NmxehJgAAKLOc/EKt3J2m77ce1Yo/jqugyEU6fvKCHg6Szj/dx+zkoACv87eVNwny0l3NAhXewF8uTv/f3p1HR1ml+QP/vrVXlspGVkgCAcImooBicNqlAcWfTdMtrUJz7Iijjj2cGR3ttl0G1/FMj7a2Dm2P/3S3nlFoYRqRdkVANg1LIpssCYTsSWWrVKpSVantvb8/qlJJJYEsVKVSyfdzTk7qXe6t+15uqCdP3nvfsROwExERUf/Gw8N9LqfUaMWW4hp8dKwOJpsrsP8H0yfgmngbLtYYMW36DADAWP37brjWQeW6m+MXk5pEREQ0JG6vjCMVJmw/VocvvjfC6vQEjukUApNS4pFm0CJOq4KtqRqrrs3EkoKFMOhVkCSuh0lERDRejdWH+1yK2e7Cp6casKW4NvAEcwBIi9fi7oWTcO/CHOSkxODAgQOorI1cO8cCrrs5PjGpSURERP0SAmjvlPF9XTuqWu2obLWhuNKEIxUm2FzewHlZCTr8+JqJmKZuw8GzdcidmR84Vm2RkW1QISFGHYlLICIiIhoxsgDKzV6c2VeOPeeaUFzVBq/sm7GiUkhYMisN9yzMxs35qVApFRFubfhwOjiNFCY1iYho3HG5+gZa8+bNg0ajiVCLIs9kc+HrSgeOtalxtKQWbQ4XbE4dtte1ADjY5/zkWA1un5OBn1yThesmJ0OhkHD06FF8wxsxiYiIaJxwy0BVqw315k7UtztQ32bAp802AN13Cc7MiMeq+ZPwk2snIjV+fDzBnNPBaaQwqUlEROPOiRMn8MaW3cjInQYAMFZdwOPwLVY/nlS32rHzjBE7zzSiuNIE340EKgAO/xkSJAApcRrkJMcgNyUWszMNWDwtBbMyDFAomMEkIiIa7fp7QA8wvIf0hLKuaOPxyjjf1IFTte04WWfGgTMdqLIYgJb6HmdJiFNLWDQ1FT+YPgFLZqUjOzkmYm2OJE4Hp5HApCYREY1LGbnTkDPj6kg3Y0TJssD39e346kwjvjrTiHNGa9DxyQkqaGQHJmdPQlKMBpa6MhTekIMbFl0foRYTERHRler9gB5g+A/pCWVdo5EQAq02F2rbHKhts6PG5EBNmx1nGyw4U2+B0yP3KiEhQa9GVoIOmYl6yE3lWHdDFm6+aWz+oby/pPZwp5VzijqFApOaREREY4wsC9g8Ek43u1BZXIMakx0natvxXXUbrJ3dD/VRKiQsmpKMZbPTsXRWOozlp7HpcBVyMg0AAJfRdw4RERFFt1A+oCfaH/bjEYCxvRMmmwuWTjcaLHpUHLbh+SN7UW92oNPdO3HZLV6rwpyJBsydmABthxGl1Y2Yfc2CwPGLJhmKMfxQxP6S2sOdVs4p6hQKTGoSEVFU4XqYwerNDhytNGHXSSuOtGiwu6UCVqcHQuiw02gGYA46P1ajxE35qbhtTjpunZGGxJjufjOWj2jTiYiIiMLG45VR2WrDOaMVpUYrzhmtOFFpRZPdADTX9DhTA3R6AdgAAJIEpMfrMClJj0lJemQnx2BaWhzmTkzA5JTYwPI7Bw60oqpOjPyFDVF/d1c6nU4AgFbbvcbnYO+S7J3UvpJp5ZyiTleKSU0iIooq43k9TLdXRlmjFd9Vm1FcaUJxZRvqzI4eZygB+O7EVEAgPU6FaZlJmJQUgxnpcVg4ORkzM+LH9NM2iYiIxoLxvHblUDg9XlgcHjR0qvBpuRNftJxCtcmOapMddW0OeOT+ko4SYjRKJMdqkKBXw9PehJunJeKW6+YhM1GPrEQdtCrliF9LuPR/d+VOKPVxuGrB4h77+t4l2XuKOKeH02jDpCYREUWdsbgephBAu1PGOaMFTRYnmqxONFudaLJ2osnqRF2bA2cb+q7lpFRIuCrLgCytCw2t7ZiaNwWJMRq0VpzB2htyx0Wyl4iIaKwZ62tXDpZXFmhod6DaZEeNP1lZbXLgdFUHatvj4Wq66D8zFsUWJ4DqoPIxGiXy0+MxMyMeMzLi4WyqxHflRsya12PK+Kka/GCSBounTRi5Cxth/d1dqYxNHPAuyd5TxDk9nEYbJjWJiIhGkNPjxYWmDuytcuCUWYVjx+vQ7nCj3a7D9roWAAcuWz5ep8K8SYlYODkJ109OxjU5iYjRqHD06FFsOmzCpCTfEzZNY3c5JyIionEh2teuvBwhBJyyhCqLF57SJjRbnGi0+P6Q2/W9yf+9/7stAcA380SvVkIrnJiZpsPCGZORkxyDnJQY5CTHIMOgC0wXB4ADB2pxumL0Txnv7yE6QGTu1O05RZzTw2m0YVKTiIhG1FheE9PtldHa4UKz1Ynmjk7f98Adl05cbLahvLmjR3CuBjrs/te+gDs5VoO0eC1S/V9p8TqkxWuRbtBhdpYBuckxQcE5ERERjV7jbRq5EAJtdjcaLZ2+5KQ/Wdlo7cTZCjvKTbH4+mAF7C4PZGHAzgM24EDf/ulJrZSQnRSD7GRfojInOQYdjZU4drEJs6+eB61KiYuninHPtcn4wQ9mjNCVDs5w//37e4hOddn3WHbiBObNmxfYx+ngNN4xqUlERIMWioRkNK+J2en2otpkx9F6J85bVbhwrgntDjfMFi12/b0Z1r99Pqh6DDoVJsVJ6Ox0YPKkLCTo1bA1lOP+gmwsXnR9mK+CiIhofBhuQimUicjRMo1cCMDlkdHp9sIjC1g8Clw0e6GvNMHlkeHyynB7BVweGW6vHNjXtd2172JVJ05bdag81wSXV4bT7YXFEotj+zvgPbgbLR1OuL2XuxNSBXg8ga14jYRJKfH+P+D6/pibbtAi1f893aBDukEHZa8/6B44UIeLNfKoX/vySv79+3uIzt+Kzg34tHCug0njCZOaRETjRH8JSWBoSclQJSRH05qYQgi4ZKDB6kFJVRtMNhdMNidMNjdMNidabS7Umx2oarWjob2zR0k10N7uf60A4AvgVQoJE+K677RM7fF6YqIes7IMyErQobi4GJsOW5Az0RdkVjcLqHkHJhERjXOjIaEY6kRkOKaRd7q9aLO7YLK50GZzw2R3oc3m37a70Grr3m5s60C70wB5X3mPGuKx7xsb8E3RMN5dC9S199hWweSWAXTHSSmxGqQZdIFkZbpBB0tjLU7XtmLq9JmI1SrRWHYCq6/Pjvgaof1N9R7unbT9JRQzcqeH7N9/ME8L5zqYNJ4wqUlEFAbhmGJ9pXX2TkgCw0tKjqaEZBchfHcWOFxeONxe2F0eONxetDvc/mDfF9yb/F+VDW2oNmvhabqITrcXstDj03oTgG8HfK94nQqpOsDp7ERWeioS9Go4mmvw02sycesN85EUo+H0cCIiGrL+knmDSayEMgk43DaE0mhJKI7UepZCCNhcXrTZXGh3uNFmd6HN7kZJpQtlNi1qyprhcHvR6fLCbIlF0R4r7F99AbvLO8R38sUmSoUElUICZA/itErEx+ihViqgUSkC3zWBbQkaldL33b+vydiAC40WJKdnQatUQKNWwFxXgaWzUrF44bWY4P+Drkal6NOCAweaYGr2ICPBN56aR0m41DsJ2N80b2BwPwuDSSj2l0QN9d2UXAeTxgsmNXt5++238dprr8FoNGLevHnYuHEjrr+eUwGJosloWLMxHFOsQ1FnpBOSHq8Mu1tGpxdod7jh8cpoc0k43exCR2kTOl1edHq8cLhkXwDv9gYlKi0OD2oa21DTpoUwVfqmRrl1+PijZsjbBjf1u5sCQPcvBDqVhFSDDskxGiTHapAcq0VKnAZJMRpkJGgxOSUWk1NikRij9t9lWYWcqb6nZFZbq5CboEJKnDZ0nUVE49ZYjUfDnXwbbF2hTNyFsq7eybzBJvJCmQQcbhtCvW7k5RKKQgh4ZAGvLOD2yv7vvm2PLEOWAY/s219t8aLdrYDR0gkhBGQBtLiU+L7FA29pE2Qh4AmUFYHtszUuVDnU6Kg1QwhAFgItdg0+LXfipOcCZFnAK0Tgu1f2neOVu7/cXt9U71qjHVXmGHxXUguPLMMjCzgccTi4ywp5z07YXZ7LTNfWATZzj20V4BHoil9UCglJsRokx2iQFKtGcqwvbgn6HqtBVdlp7DvbgBlzr4VKIUGSJN8alNcN/S7JAwfasMXWjLy8lMC+MqMd3rrTsExQwQKg63nkofrZdjqdAACttjvO6p0E7C9ROJhyXXonAXtP8x7KepYDJRT7Wy+Td1MSDQ+Tmj18+OGHePzxx/HOO+9g0aJFePPNN3H77bejtLQUaWlpkW4ejTGhmAo8mDpHOpkXijaE+o7E4ST/htMGIbqDYodbRnL2NKRMng0hAJtHQkOHBxearL6gWQYEBIToKuv/7t8netQp/MfPtrigysiHlJoHWQjAoUBxvROm08agINobCLCDvyqr7CizqmCsNEHIAjIAc7sK75/qwM6mM4FfELq+fO0UQftbTGbUtGnwnb0OshBw2DQ4u7cN+qPf+Mr0+KWiqw6XpztB2R2064GGSv9rHfY2mYH9l18kPpgCcLv9r4P/zK9USIhRK6HTKGHQqfwJSn+S0h/ctzfWoriiGbl506BXK9FSeRb33ZAbFet6EtHYFg3x6FATWLIs4PLK2F90BH/4216k506HDEAICcaai/iF1YsFCxZAFr7PwZ6fkaJrn0AgKQUIHDt+Alt2FyMlKyfwPs0NNbir2YOrrroKsv/zE/6EVFdiSgA4ffoM/v7NCSSnTwQgobWpDv+vzo38GTMC79v1fsHv311H17HzFy5g77FSGFLSAQDtrU34QbkTk6dMCbxvF9Fjo+d+fzNRVeWELX0emmOzAAC2dC22lTlR4iwLig26CndtV1V3wpo2D8aYzMBhS5oWW8514ltbcEKl7/v2bCBQU9uJ9rRrIOkyICDQnqbD5rOdOGA926f9XfGJLATq6utxvKwasQlJgX7vsLZj1gkHUiakwuP1xQa+771fC3i8cuAcq80Oa2c8FAcvQu5KFnoN+OJzC+TPP4P3kk/CvpR44GhNj+04FB22A4cHijtigNLmHtt6nDnnBM6VDvH9AUANuBw9tpWwewUAd2CPRqlAYowaSTEaJMao4XVY0GS2IzU9PRDXmKrLMFNvw7Vz8hGvkaBX+WJWwAWtVgLgAeB7AOF1C7t/Hg80KnFUKaBWdt89OdgkYO99/SXyhvtQm0vdsbjrTAOyp83p3ndwJ5T6OFy1YHGPfcFJwP4ThQOXu5Thrmc5WIOZRk5EA2NSs4c33ngDDz30ENatWwcAeOedd/Dpp5/iz3/+M5566qkItw6oMzvw+ck61NXVBQUkWVlZUCj7LpDcM0nSte31elFfX999DoDMzMyg8n2CHSGC9nu9XjQYjUHnZGRkQOmvo3f53jxeDxqNjUH70jPSoVQqByzbddjr9aKxMbgN6WnpgTZcrmx3+cY+x/qrIyjYC6pDDqoDANLS0oLKdwV7AiIoqBYAGoxGfFdWgxhDIvyHYbe24+rvLJiQmgb0Curl3vWgZ0Dp2zaZTCirboQ2Nh4A0GnvwPSidiQmJQYFn4Er61G+q71erwyL1RroFAGBuLh4SAopKAHXO/kG/z6LtQP1LWaotb4gyuV0IvNrE2Ji4wKFe7Yj+JcWH7vdjmazFSq1FgK+gCdlZyt0en3gWrvfE0H7hPD9stWhmYPvW3wJSI9mDh75rAWaXbv7TR721ydutxsOpxuSUunrc1mGdkez79+31/t2/VXf0yfI1gP1Ff7XOuw0moAv9+PKaIHmusDroqJ2oKhkCOXVQHtr0HaZ1Q6UVVyyRF9KoNMeeN3S6gZazUMoDwC+wFqlUECS3UiKUSPJEAu9WgmdWgm9Wgm9RhnY1qmViNUoYdCr0VxfjeKKFkzKnQqNSoHmqjL8bMFE3HDdAujVyn6nO/V29GgLahpkpMX7xmnbKJn+REQ02uNRAPj5/+xDaX0bVFo9BCTIwve5GbdnDxQqDTz+B450PVgk6NMxbgHQ82Modh6e2m8D9g/j8zFmNmDusa2fhVeP2IEjRwYuq89HhcX/WjcdfzzuAI4fH3obAEA7Gejoep2Lv55zAueGm6DQARWmwOvS807g/PnBlatsC9q+UO4CyssvWeLStEB1W+B1+UUXcPHiZUsAADQZaO6Zt1On4WCtG6itv2SRS1MAzp7TqyUEsqWXoJQAlVIBlUKC0v8lez1wur1QazRQSBIkCfB0OqBXehGj10MpARIEFBKgUiigUPjq6bBa0eFVIT4xKVCuo60FiSo3JiQnQyEBCgkQsheSJEGtUvpW15a9UEiAWqWESiFBowSaGurQ7NZh4pRpUCkkqJQKNFz4HtP1dsyZmQ+tSkKcWgI8TkgSAsnJEyfO40xsAqb3SO4dPHIO37aZYJFiA/v6S9z1TigONhHZfxIweN+lEnnDSQJe7o7F3nUpYxMHTAL214bBlBssJiKJRh8mNf1cLhdKSkrw9NNPB/YpFAosXboURUV9F092Op2Bv1oBQLv/YREWi6XPuaFyqqIFL310rJ8jTVdYc/PAp4S9jlC0oWUU1DHU8jFAq6vHth4N50zAOdMlSwxMA5i7xqYaLRVtQEXbZUsMrH3gU4JIgKv758PSYAEwjJ8NV/di47YWKwDrECvoUd5+mdMGyd3jmoZCgoAkBFRKX5CrlACFJPU4HnyvoST12vbv83jcsDvdUKs1vm1XJxL0asTF6KGQfOco/HUr/a+VkgQFfK+tVgsa2x3Qx8VDgm/ytb2jHZNTYjAhpTtAVyp6lIcUqFupkNDc2ICzDRYkJKdCkgBLayPmTUxAdvYkKCVAqeh+f6X/lwCVAtApJahVEjRKCZUXyrD3nBHp2XkAfAHt0uwMzJgxA747DC7DCThaSuGpMcKODtgBtNVcRIW+GRqv4/JleygtLUVNmRFOhz3QhhJVCzo6OgYoGZryoapjtLWpd/lQ1BGNbQjHdTTVXETHnJSwxhlAdxwjBvorI4XFUONRIDIxaVmjFVaPEvC4gvY7zDYAtkHUIAKfTV6PG0KWofTfQSYJAa/HDUiAUun7VUWCgOz1IlavhVajgQQJLrcTdpcHarXGf47vczpep4ROpw98tnZ2OtBm6YBS5f+1Rwi4Ou1QafSIjfd9HnZY2uB1OaHVx/jbALhdnchMTYTBf44kAVaLBbVNrdBq9OhKrtmtZmhj45GQ5JuKa25pgLvTidi4+MDV2sytkJRKxMQnBD7fO8wmSEolYuO7E00d5lboDIlISknz192OzAQdUpL9CR8JMLWacKHGCF1MTKCcta0FekMSkidkBPa1Gmvg7HQgLiHRd46pBZJCifjEpMA5FlMzJIUK8YmJPfb56pqQ5rtbtLm+Gk6HDfGJXUknAaupBVAoYQjUJWBpbUZMQgpS07MAfxzTWFOOTnsHEpNSAAi0GWsgKZVImZAGCAEJAi21lVAolZiQkQnJv6+xqhxJmdnIy58diInOHy+Cta0VWdm5kIRA7fnTUKjVmJQ7FYDs6/vGBtxzWwHmzp3r//cROHXqNPafb0HW5O4U3KlTe2A2NSNpSj4AoPrcKSi0Wkyakg8vfPdO1p87hdS8mZh2TfeyD6eO7UGlqRkef7neZfvb7tqXmjcTmUorfPdUAtWH9uCkqRmHB6grNW8mvO7un++GqgtQ6uPg6uz+jPO4XRAqV9C+5rpK/PHUUUw6dPaK6+q9z+N2oaHiHM4dNfSpq799vevqWXYw5wxUf++6hlputNY1GtrAulhXf/vqK8/DNjs5rHHGUOJRSTBqBQDU19dj4sSJ+Pbbb1FQUBDY/+STT2Lfvn04fPhw0PkvvPACXnzxxZFuJhEREVHY1dTUYNKkSZFuxrgz1HgUYExKREREY9Ng4lHeqTlMTz/9NB5//PHAtizLMJlMSElJgdTjDqxQs1gsyM7ORk1NDQwGw8AFaEDs0/Bgv4Ye+zT02KfhwX4NvZHqUyEErFYrsrKywvYeFFqRiEn5Mx4e7NfQY5+GHvs0PNivocc+DY+R6NehxKNMavpNmDABSqWyzxqJjY2NyMjI6HO+VqsNWkAZABJ7TN0IN4PBwB/MEGOfhgf7NfTYp6HHPg0P9mvojUSfJiQkDHwShcVQ41EgsjEpf8bDg/0aeuzT0GOfhgf7NfTYp+ER7n4dbDw68NMUxgmNRoMFCxZg9+7dgX2yLGP37t1B03+IiIiIiMKB8SgRERHR4PFOzR4ef/xxFBYWYuHChbj++uvx5ptvwmazBZ4+SUREREQUToxHiYiIiAaHSc0e7r33XjQ3N+O5556D0WjENddcgy+++ALp6emRblqAVqvF888/32eaEQ0f+zQ82K+hxz4NPfZpeLBfQ499On4wHh2/2K+hxz4NPfZpeLBfQ499Gh6jrV/59HMiIiIiIiIiIiKKKlxTk4iIiIiIiIiIiKIKk5pEREREREREREQUVZjUJCIiIiIiIiIioqjCpCYRERERERERERFFFSY1R6HJkydDkqQ+X+vXrwcAdHZ2Yv369UhJSUFcXBxWrVqFxsbGCLd69BuoX2+55ZY+xx555JEIt3p083q92LBhA6ZMmQK9Xo+pU6fi5ZdfRs/njwkh8NxzzyEzMxN6vR5Lly7F+fPnI9jq0W0wfXr//ff3GavLly+PYKtHP6vVisceewy5ubnQ6/VYvHgxjh49GjjOcTo8A/Urx+rA9u/fjxUrViArKwuSJGH79u1BxwczNk0mE9auXQuDwYDExET84z/+Izo6OkbwKmisYkwaeoxHQ4/xaHgwJg0PxqShx3j0ykV1PCpo1GlqahINDQ2Br6+++koAEF9//bUQQohHHnlEZGdni927d4vi4mJxww03iMWLF0e20VFgoH69+eabxUMPPRR0Tnt7e2QbPcq98sorIiUlRXzyySeioqJCbN26VcTFxYm33norcM5vf/tbkZCQILZv3y5OnDghfvzjH4spU6YIh8MRwZaPXoPp08LCQrF8+fKgsWoymSLY6tHvnnvuEbNnzxb79u0T58+fF88//7wwGAyitrZWCMFxOlwD9SvH6sA+++wz8eyzz4pt27YJAOKjjz4KOj6Ysbl8+XIxb948cejQIXHgwAExbdo0sWbNmhG+EhqLGJOGHuPR0GM8Gh6MScODMWnoMR69ctEcjzKpGQUeffRRMXXqVCHLsjCbzUKtVoutW7cGjp89e1YAEEVFRRFsZfTp2a9C+ILIRx99NLKNijJ33nmneOCBB4L23XXXXWLt2rVCCCFkWRYZGRnitddeCxw3m81Cq9WKzZs3j2hbo8VAfSqE74N55cqVI9yy6GW324VSqRSffPJJ0P758+eLZ599luN0mAbqVyE4VoeqdxA5mLF55swZAUAcPXo0cM7nn38uJEkSdXV1I9Z2Gh8Yk4Ye49Erx3g0PBiThh5j0tBjPBp60RaPcvr5KOdyufD+++/jgQcegCRJKCkpgdvtxtKlSwPnzJw5Ezk5OSgqKopgS6NL737t8sEHH2DChAm46qqr8PTTT8Nut0ewlaPf4sWLsXv3bpSVlQEATpw4gYMHD+KOO+4AAFRUVMBoNAaN14SEBCxatIjj9RIG6tMue/fuRVpaGmbMmIFf/vKXaG1tjURzo4LH44HX64VOpwvar9frcfDgQY7TYRqoX7twrA7fYMZmUVEREhMTsXDhwsA5S5cuhUKhwOHDh0e8zTR2MSYNPcajocF4NDwYk4YeY9LQYzwafqM9HlWFtXa6Ytu3b4fZbMb9998PADAajdBoNEhMTAw6Lz09HUajceQbGKV69ysA/PznP0dubi6ysrJw8uRJ/OY3v0FpaSm2bdsWuYaOck899RQsFgtmzpwJpVIJr9eLV155BWvXrgWAwJhMT08PKsfxemkD9SkALF++HHfddRemTJmC8vJyPPPMM7jjjjtQVFQEpVIZwdaPTvHx8SgoKMDLL7+MWbNmIT09HZs3b0ZRURGmTZvGcTpMA/UrwLF6pQYzNo1GI9LS0oKOq1QqJCcnc/xSSDEmDT3Go6HBeDQ8GJOGHmPS0GM8Gn6jPR5lUnOU+9Of/oQ77rgDWVlZkW7KmNJfvz788MOB13PnzkVmZiaWLFmC8vJyTJ06NRLNHPW2bNmCDz74AJs2bcKcOXNw/PhxPPbYY8jKykJhYWGkmxeVBtOnq1evDpw/d+5cXH311Zg6dSr27t2LJUuWRKrpo9r//u//4oEHHsDEiROhVCoxf/58rFmzBiUlJZFuWlQbqF85VonGDsakocd4NDQYj4YHY9LwYEwaeoxHxzdOPx/FqqqqsGvXLjz44IOBfRkZGXC5XDCbzUHnNjY2IiMjY4RbGJ3669f+LFq0CABw4cKFkWhWVPr1r3+Np556CqtXr8bcuXNx33334d/+7d/wn//5nwAQGJO9n4TK8XppA/Vpf/Ly8jBhwgSO1cuYOnUq9u3bh46ODtTU1ODIkSNwu93Iy8vjOL0Cl+vX/nCsDs1gxmZGRgaampqCjns8HphMJo5fChnGpKHHeDR0GI+GB2PS8GBMGnqMR8NrtMejTGqOYn/5y1+QlpaGO++8M7BvwYIFUKvV2L17d2BfaWkpqqurUVBQEIlmRp3++rU/x48fBwBkZmaOQKuik91uh0IR/N+IUqmELMsAgClTpiAjIyNovFosFhw+fJjj9RIG6tP+1NbWorW1lWN1EGJjY5GZmYm2tjZ8+eWXWLlyJcdpCPTXr/3hWB2awYzNgoICmM3moDs89uzZA1mWA8kQoivFmDT0GI+GDuPR8GBMGl6MSUOP8Wh4jPp4NKyPIaJh83q9IicnR/zmN7/pc+yRRx4ROTk5Ys+ePaK4uFgUFBSIgoKCCLQy+lyqXy9cuCBeeuklUVxcLCoqKsTHH38s8vLyxE033RShlkaHwsJCMXHiRPHJJ5+IiooKsW3bNjFhwgTx5JNPBs757W9/KxITE8XHH38sTp48KVauXCmmTJkiHA5HBFs+eg3Up1arVfzqV78SRUVFoqKiQuzatUvMnz9fTJ8+XXR2dka49aPXF198IT7//HNx8eJFsXPnTjFv3jyxaNEi4XK5hBAcp8N1uX7lWB0cq9Uqjh07Jo4dOyYAiDfeeEMcO3ZMVFVVCSEGNzaXL18urr32WnH48GFx8OBBMX36dLFmzZpIXRKNMYxJQ4/xaGgxHg0PxqThwZg09BiPXrlojkeZ1BylvvzySwFAlJaW9jnmcDjEP//zP4ukpCQRExMjfvrTn4qGhoYItDL6XKpfq6urxU033SSSk5OFVqsV06ZNE7/+9a9Fe3t7hFoaHSwWi3j00UdFTk6O0Ol0Ii8vTzz77LPC6XQGzpFlWWzYsEGkp6cLrVYrlixZ0u+4Jp+B+tRut4vbbrtNpKamCrVaLXJzc8VDDz0kjEZjhFs+un344YciLy9PaDQakZGRIdavXy/MZnPgOMfp8FyuXzlWB+frr78WAPp8FRYWCiEGNzZbW1vFmjVrRFxcnDAYDGLdunXCarVG4GpoLGJMGnqMR0OL8Wh4MCYND8akocd49MpFczwqCSFEeO8FJSIiIiIiIiIiIgodrqlJREREREREREREUYVJTSIiIiIiIiIiIooqTGoSERERERERERFRVGFSk4iIiIiIiIiIiKIKk5pEREREREREREQUVZjUJCIiIiIiIiIioqjCpCYRERERERERERFFFSY1iYiIiIiIiIiIKKowqUlERERERERERERRhUlNIqIR0tzcjF/+8pfIycmBVqtFRkYGbr/9dnzzzTdDqueTTz7BzTffjPj4eMTExOC6667Du+++G3TOiRMnsGbNGmRnZ0Ov12PWrFl46623Qng1RERERBSNQhGTer1e/P73v8fcuXOh0+mQlJSEO+64o08d27Ztw7Jly5CamgqDwYCCggJ8+eWXob4kIhqnmNQkIhohq1atwrFjx/Dee++hrKwMO3bswC233ILW1tZB17Fx40asXLkSN954Iw4fPoyTJ09i9erVeOSRR/CrX/0qcF5JSQnS0tLw/vvv4/Tp03j22Wfx9NNP4w9/+EM4Lo2IiIiIosSVxqRCCKxevRovvfQSHn30UZw9exZ79+5FdnY2brnlFmzfvj1w7v79+7Fs2TJ89tlnKCkpwa233ooVK1bg2LFjYbo6IhpPJCGEiHQjiIjGOrPZjKSkJOzduxc333xzv+dIkoQ//vGP2LFjB/bu3YvMzEy8+uqr+NnPfgYAqKmpwdSpU/Ev//IveP3114PKbty4Ef/6r/+KQ4cOYdGiRf3Wv379epw9exZ79uwJ7cURERERUVQYTEz6xhtv4C9/+QsuXryI5ORkrFixAq+++iri4uIAAB9++CFWr16NHTt2YMWKFUFlV61ahX379qGqqgqxsbH91j9nzhzce++9eO6550J7cUQ07vBOTSKiERAXF4e4uDhs374dTqfzkudt2LABq1atwokTJ7B27VqsXr0aZ8+eBQD83//9H9xud9AdmV3+6Z/+CXFxcdi8efMl625vb0dycvKVXwwRERERRaXBxKQKhQL//d//jdOnT+O9997Dnj178OSTTwaOb9q0Cfn5+X0SmgDwxBNPoLW1FV999VW/dcuyDKvVypiUiEKCSU0iohGgUqnw7rvv4r333kNiYiJuvPFGPPPMMzh58mTQeXfffTcefPBB5Ofn4+WXX8bChQuxceNGAEBZWRkSEhKQmZnZp36NRoO8vDyUlZX1+/7ffvstPvzwQzz88MOhvzgiIiIiigqDiUkfe+wx3HrrrZg8eTJ++MMf4j/+4z+wZcuWwPGysjLMmjWr3/q79l8qJv3d736Hjo4O3HPPPSG8KiIar5jUJCIaIatWrUJ9fT127NiB5cuXY+/evZg/f37QQ34KCgqCyhQUFATu1Byu77//HitXrsTzzz+P22677YrqIiIiIqLoNlBMumvXLixZsgQTJ05EfHw87rvvPrS2tsJutwfqGM4qdps2bcKLL76ILVu2IC0tLVSXQ0TjGJOaREQjSKfTYdmyZdiwYQO+/fZb3H///Xj++ecHVTY/Px/t7e2or6/vc8zlcqG8vBz5+flB+8+cOYMlS5bg4Ycfxr//+7+H5BqIiIiIKLpdKiatrKzEj370I1x99dX429/+hpKSErz99tsAfPEm4ItJL/VH9679vWPSv/71r3jwwQexZcsWLF26NIxXRkTjCZOaREQRNHv2bNhstsD2oUOHgo4fOnQoMI1n1apVUKvVfR4SBADvvPMObDYb1qxZE9h3+vRp3HrrrSgsLMQrr7wSpisgIiIiomjXFZOWlJRAlmW8/vrruOGGG5Cfn9/nD+qrV6/G+fPn8fe//71PPa+//jpSUlKwbNmywL7Nmzdj3bp12Lx5M+68886wXwsRjR+qSDeAiGg8aG1txd13340HHngAV199NeLj41FcXIxXX30VK1euDJy3detWLFy4EP/wD/+ADz74AEeOHMGf/vQnAEBOTg5effVVPPHEE9DpdLjvvvugVqvx8ccf45lnnsETTzwRePL5999/jx/+8Ie4/fbb8fjjj8NoNAIAlEolUlNTR74DiIiIiCjiBopJp02bBrfbjY0bN2LFihX45ptv8M477wTVsXr1amzduhWFhYV47bXXsGTJElgsFrz99tvYsWMHtm7dGnjy+aZNm1BYWIi33noLixYtCsSker0eCQkJI379RDS2SGI4i2EQEdGQOJ1OvPDCC9i5cyfKy8vhdruRnZ2Nu+++G8888wz0ej0kScLbb7+N7du3Y//+/cjMzMR//dd/9VlIfceOHfjd736H7777Dl6vF3PmzMH69euxbt26wDkvvPACXnzxxT7tyM3NRWVlZbgvl4iIiIhGocHEpL///e/x2muvwWw246abbsLatWvxi1/8Am1tbUhMTAQAeDwevPnmm3j33Xdx/vx56HQ6FBQUYMOGDbjxxhsD73fLLbdg3759fdpRWFgYtK48EdFwMKlJRDRKSJKEjz76CD/5yU8i3RQiIiIiIiKiUY1rahIREREREREREVFUYVKTiIiIiIiIiIiIogofFERENEpwNRAiIiIiIiKiweGdmkRERERERERERBRVmNQkIiIiIiIiIiKiqMKkJhEREREREREREUUVJjWJiIiIiIiIiIgoqjCpSURERERERERERFGFSU0iIiIiIiIiIiKKKkxqEhERERERERERUVRhUpOIiIiIiIiIiIiiyv8HaeZcvw+wdBQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualization of the Distribution of SpO2 values\n",
        "plt.figure(figsize=(16,6))\n",
        "\n",
        "\n",
        "plt.subplot(1,2,1) # a 1-row, 2-column figure: go to the first subplot\n",
        "sns.histplot(data_train_imputed_final['SpO2'], kde = True)\n",
        "plt.title ('Distribution of SpO2 Values')\n",
        "plt.xlabel('SpO2')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "\n",
        "# Visualization of the Distribution of SaO2 values\n",
        "plt.subplot(1,2,2)\n",
        "sns.histplot(data_train_imputed_final['SaO2'], kde = True)\n",
        "plt.title ('Distribution of SaO2 Values')\n",
        "plt.xlabel('SaO2')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Adjust the space between the 2 subplots\n",
        "plt.subplots_adjust (wspace = 0.2)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "90e8fd7e",
      "metadata": {
        "id": "90e8fd7e"
      },
      "source": [
        "- Question: - Question: What key insights can you derive from theses plots?\n",
        "\n",
        "- Your answer: ....."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Comparison of Arterial Oxygen Saturation by Pulse Oximetry Readings"
      ],
      "metadata": {
        "id": "UsjxDOp6ntkr"
      },
      "id": "UsjxDOp6ntkr"
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy of the relevant columns from the original DataFrame, filtering only 'White' and 'Black' races\n",
        "df = data_train_imputed_final.copy()[['SpO2','SaO2','race_ethnicity']][data_train_imputed_final['race_ethnicity'].isin(['White','Black'])]\n",
        "\n",
        "# Import the linregress function from scipy.stats for performing linear regression, and t for statistical tests\n",
        "from scipy.stats import linregress, t\n",
        "\n",
        "# Create a new DataFrame that only includes rows where SpO2 is greater than or equal to 89\n",
        "df89 = df.copy()[df.SpO2 >= 89]\n",
        "\n",
        "# Group the DataFrame by 'race_ethnicity'\n",
        "races = df89.groupby('race_ethnicity')\n",
        "\n",
        "# Create a matplotlib figure and axes for plotting\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Define the x-value where we want to calculate the intercept\n",
        "x_start = 89\n",
        "\n",
        "# Define offsets for plotting each race separately to avoid overlapping points\n",
        "offsets = {'White': -0.2, 'Black': 0.}\n",
        "\n",
        "# Loop through each group (race) and perform linear regression\n",
        "for name, race in races:\n",
        "    # Perform linear regression between SpO2 and SaO2 for the current race group\n",
        "    slope, intercept, r_value, p_value, std_err = linregress(race['SpO2'], race['SaO2'])\n",
        "\n",
        "    # Calculate the intercept at x = 89 using the regression line equation\n",
        "    intercept_at_x_start = slope * x_start + intercept\n",
        "\n",
        "    # Plot the regression line with an offset for each race group to avoid overlapping points\n",
        "    offset_x = race['SpO2'] + offsets[name]\n",
        "    ax.plot(offset_x, race['SaO2'], 'o', label=f'Race {name}')\n",
        "    ax.plot(race['SpO2'], slope * race['SpO2'] + intercept, label=f'Race {name} fit: y={slope:.2f}(x - {x_start} )+{intercept_at_x_start:.2f}')\n",
        "\n",
        "    # Print the slope and intercept for each race group\n",
        "    print(f'Race {name}:')\n",
        "    print(f'  Slope: {slope:.2f}')\n",
        "    print(f'  Intercept at x={x_start}: {intercept_at_x_start:.2f}')\n",
        "    print()\n",
        "\n",
        "\n",
        "# Add a legend to the plot to distinguish between the different races\n",
        "ax.legend()\n",
        "\n",
        "# Label the x-axis as 'SpO2'\n",
        "plt.xlabel('SpO2')\n",
        "\n",
        "# Label the y-axis as 'SaO2'\n",
        "plt.ylabel('SaO2')\n",
        "\n",
        "# Set the title of the plot\n",
        "plt.title('Regression Lines for Different Races')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "7eFM7vIAvWgo",
        "outputId": "ac6ecceb-0586-4943-e6a5-1a9914a976c0"
      },
      "id": "7eFM7vIAvWgo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Race Black:\n",
            "  Slope: 0.85\n",
            "  Intercept at x=89: 88.32\n",
            "\n",
            "Race White:\n",
            "  Slope: 0.79\n",
            "  Intercept at x=89: 89.40\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: What key insights can you derive from this plot?\n",
        "- Your answer:\n"
      ],
      "metadata": {
        "id": "Z3VJHqoIphMJ"
      },
      "id": "Z3VJHqoIphMJ"
    },
    {
      "cell_type": "markdown",
      "id": "8d8d83f0",
      "metadata": {
        "id": "8d8d83f0"
      },
      "source": [
        "## Analysis of Hypoxemia using SpO2 and SaO2 individually\n",
        "\n",
        "* Define hypoxemia using a threshold for SpO2 and SaO2.\n",
        "* Compare the incidence of hypoxemia across different demographic groups.\n",
        "* Highlight any disparities that may indicate bias in SpO2 measurements.\n",
        "\n",
        "Goal: To compare SpO2 and SaO2 values side by side and examine the incidence of hypoxemia using both measurements. This step highlights the discrepancies and biases in SpO2 measurements by analyzing hypoxemia based on SaO2 measurements and compare it with SpO2-based analysis. This step emphasizes the importance of SaO2 measurements in revealing hidden hypoxemia that SpO2 alone might miss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9052964c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9052964c",
        "outputId": "756da6e4-19e2-41a7-ec6e-f61764194d4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SpO2:\n",
            "\n",
            "       race_ethnicity  hypoxemia_incidence\n",
            "0               Asian             2.142857\n",
            "1               Black             3.082902\n",
            "2  Hispanic OR Latino             2.190645\n",
            "3               White             2.819637\n",
            "\n",
            "\n",
            "SaO2:\n",
            "\n",
            "       race_ethnicity  hypoxemia_incidence\n",
            "0               Asian             4.428571\n",
            "1               Black             5.673575\n",
            "2  Hispanic OR Latino             4.440497\n",
            "3               White             4.557632\n"
          ]
        }
      ],
      "source": [
        "# Please add your code here ...\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "366327f9",
      "metadata": {
        "id": "366327f9"
      },
      "source": [
        "### Hypoxemia Incidence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6f9bfd62",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "6f9bfd62",
        "outputId": "429f3cfd-4a38-412a-da3f-f1a12864e7fc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualization\n",
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "\n",
        "# Define the common y-axis limit\n",
        "y_max = max(hypoxemia_by_race_SpO2['hypoxemia_incidence'].max(), hypoxemia_by_race_SaO2['hypoxemia_incidence'].max())\n",
        "\n",
        "# a 1-row, 2-column figure: go to the first subplot\n",
        "plt.subplot(1,2,1)\n",
        "sns.barplot(x='race_ethnicity', y='hypoxemia_incidence', data = hypoxemia_by_race_SpO2)\n",
        "plt.title ('Hypoxemia Incidence - SpO2')\n",
        "plt.xlabel('Race/Ethnicity')\n",
        "plt.ylabel ('Hypoxemia Incidence (%)')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.ylim(0,y_max)\n",
        "\n",
        "\n",
        "# a 1-row, 2-column figure: go to the second subplot\n",
        "plt.subplot(1,2,2)\n",
        "sns.barplot(x='race_ethnicity', y='hypoxemia_incidence', data = hypoxemia_by_race_SaO2)\n",
        "plt.title ('Hypoxemia Incidence - SaO2')\n",
        "plt.xlabel('Race/Ethnicity')\n",
        "plt.ylabel ('Hypoxemia Incidence (%)')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.ylim(0,y_max)\n",
        "\n",
        "# Adjust the space between the 2 subplots\n",
        "plt.subplots_adjust (wspace = 0.2)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed30a59d",
      "metadata": {
        "id": "ed30a59d"
      },
      "source": [
        "- Question: What key insights can you derive from theses plots?\n",
        "- Your answer: ...."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4053ee6a",
      "metadata": {
        "id": "4053ee6a"
      },
      "source": [
        "## Hidden Hypoxemia\n",
        "This brings us to the concept of hidden hypoxemia. Hidden hypoxemia occurs when patients exhibit low blood oxygen levels that are not detected by pulse oximetry. The accuracy issues and dataset imbalances can exacerbate this problem, particularly in non-white populations. In the next section, we will explore the prevalence of hidden hypoxemia across different racial and ethnic groups, highlighting the critical need for accurate and equitable diagnostic tools in clinical settings.\n",
        "\n",
        "\n",
        "* Identify hidden hypoxemia using both SpO2 and SaO2 measurements.\n",
        "* Compare the incidence of hidden hypoxemia across different demographic groups.\n",
        "* Highlight how the bias in SpO2 measurements can lead to hidden hypoxemia, particularly in certain demographic groups.\n",
        "\n",
        "Goal: To identify hidden hypoxemia (patients with normal SpO2 but low SaO2) and analyze its prevalence across different demographics. This step uncovers the hidden bias in SpO2 measurements and shows how certain groups are disproportionately affected.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "522eac11",
      "metadata": {
        "id": "522eac11"
      },
      "source": [
        "### Hidden Hypoxemia - SpO2 & SaO2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dcadab09",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dcadab09",
        "outputId": "e07d3ee1-6b24-42e3-fb36-953e19de294b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       race_ethnicity  hidden_hypoxemia_incidence\n",
            "0               Asian                    3.000000\n",
            "1               Black                    4.352332\n",
            "2  Hispanic OR Latino                    3.611604\n",
            "3               White                    3.435805\n"
          ]
        }
      ],
      "source": [
        "# Please add your code here ...\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2133315d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "2133315d",
        "outputId": "c8ab0c14-5396-4308-9f17-bc61b41d24c5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        " # Visualization\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.barplot(x='race_ethnicity', y='hidden_hypoxemia_incidence', data=hidden_hypoxemia_by_race)\n",
        "plt.title('Hidden Hypoxemia Incidence by Race/Ethnicity')\n",
        "plt.xlabel('Race/Ethnicity')\n",
        "plt.ylabel('Hidden Hypoxemia Incidence (%)')\n",
        "plt.xticks(rotation = 45)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83e07335",
      "metadata": {
        "id": "83e07335"
      },
      "source": [
        "- Question: What key insights can you derive from this plot?\n",
        "- Your answer: ...."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d436996f",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "id": "d436996f"
      },
      "source": [
        "### Conclusion:\n",
        "\n",
        "* Objective: To identify hidden hypoxemia by considering both SpO2 and SaO2 measurements and to compare its incidence across different racial/ethnic groups.\n",
        "* Findings: The analysis of hidden hypoxemia shows even more pronounced disparities, with Black patients having the highest incidence.\n",
        "* Implication: The high incidence of hidden hypoxemia in Black patients indicates that relying solely on SpO2 can miss cases of low blood oxygen levels, particularly in this group. This highlights the importance of considering SaO2 measurements to ensure accurate diagnosis and treatment.\n",
        "\n",
        "The analysis highlights the importance of considering both SpO2 and SaO2 measurements in clinical settings, especially for patients from racial/ethnic groups that may be more susceptible to inaccuracies in SpO2 readings."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be15b4f0-d655-4b61-bd1c-0a9f18f7f4b6",
      "metadata": {
        "id": "be15b4f0-d655-4b61-bd1c-0a9f18f7f4b6"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3058fc5c-5d71-4c94-98f0-aad39b9dba93",
      "metadata": {
        "id": "3058fc5c-5d71-4c94-98f0-aad39b9dba93"
      },
      "source": [
        "## SpO2 modifications - exaggerating the bias\n",
        "This first step is important in the tutorial as it simulates the impact of a known bias in medical devices, specifically pulse oximeters, which often overestimate blood oxygen levels in Black patients. By artificially increasing SpO2 values for Black patients by 10%, we create a dataset that exaggerates this bias. This allows us to analyze how such inaccuracies affect downstream clinical predictions, such as mortality, and highlights the need for fair and accurate data in healthcare machine learning models.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7fe2eaaa-3b95-4785-b924-764582bcbdf5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fe2eaaa-3b95-4785-b924-764582bcbdf5",
        "outputId": "29716277-2411-44f7-f378-b536e4738ba8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline SpO2 Distribution:\n",
            "count    36111.000000\n",
            "mean        97.113151\n",
            "std          3.855122\n",
            "min         70.000000\n",
            "25%         96.000000\n",
            "50%         98.000000\n",
            "75%        100.000000\n",
            "max        100.000000\n",
            "Name: SpO2, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Display the baseline distribution of SpO2\n",
        "print(\"Baseline SpO2 Distribution:\")\n",
        "print(data_train_imputed_final['SpO2'].describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d9ad6638-8432-44b3-b558-37326425a9e9",
      "metadata": {
        "id": "d9ad6638-8432-44b3-b558-37326425a9e9"
      },
      "outputs": [],
      "source": [
        "# Please add your code here ...\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7710ab66-2e4c-4f82-bb26-f2b3ee5a754d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7710ab66-2e4c-4f82-bb26-f2b3ee5a754d",
        "outputId": "831bb5cb-512e-4b8a-cd7b-84a37ddffb99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before modification:\n",
            "count    3860.000000\n",
            "mean       97.663731\n",
            "std         3.904454\n",
            "min        70.000000\n",
            "25%        97.000000\n",
            "50%        99.000000\n",
            "75%       100.000000\n",
            "max       100.000000\n",
            "Name: SpO2, dtype: float64\n",
            "\n",
            "\n",
            "After modification:\n",
            "count    3860.000000\n",
            "mean       99.730311\n",
            "std         1.678041\n",
            "min        80.000000\n",
            "25%       100.000000\n",
            "50%       100.000000\n",
            "75%       100.000000\n",
            "max       100.000000\n",
            "Name: SpO2_exaggerated, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Compare both distributions for Black patients before and after modification\n",
        "print(\"Before modification:\")\n",
        "print(data_train_imputed_final.loc[data_train_imputed_final['race_ethnicity'] == 'Black', 'SpO2'].describe())\n",
        "\n",
        "print (\"\\n\")\n",
        "print(\"After modification:\")\n",
        "print(data_train_imputed_final.loc[data_train_imputed_final['race_ethnicity'] == 'Black', 'SpO2_exaggerated'].describe())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29a1b3bd-2284-4baf-a448-0b8f572daafe",
      "metadata": {
        "id": "29a1b3bd-2284-4baf-a448-0b8f572daafe"
      },
      "source": [
        "There is a 1% difference in the median which shows the impact of the introduced bias.\n",
        "N.B: The median is derived from the .describe() function, which provides various statistical summaries of the data, including the median (50% value)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5e28c6b5-832f-491c-bd58-27cbef68f5b3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5e28c6b5-832f-491c-bd58-27cbef68f5b3",
        "outputId": "38f99ae5-2f75-44c7-db73-f88b7d79a7a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['source_db', 'comorbidity_score_value', 'unique_subject_id',\n",
              "       'unique_hospital_admission_id', 'unique_icustay_id', 'admission_age',\n",
              "       'sex_female', 'race_ethnicity', 'BMI_admission', 'weight_admission',\n",
              "       'height_admission', 'sofa_past_overall_24hr',\n",
              "       'sofa_future_overall_24hr', 'vitals_heart_rate', 'vitals_mbp_ni',\n",
              "       'vitals_resp_rate', 'vitals_tempc', 'cbc_wbc', 'cbc_hematocrit',\n",
              "       'cbc_platelet', 'coag_inr', 'bmp_sodium', 'bmp_potassium',\n",
              "       'bmp_chloride', 'bmp_bicarbonate', 'bmp_bun', 'bmp_creatinine',\n",
              "       'bmp_glucose', 'bmp_aniongap', 'bmp_calcium', 'bmp_lactate', 'hfp_alt',\n",
              "       'hfp_alp', 'hfp_ast', 'hfp_bilirubin_total', 'hfp_albumin',\n",
              "       'others_ck_cpk', 'in_hospital_mortality', 'pH', 'SpO2', 'SaO2',\n",
              "       'hidden_hypoxemia', 'SpO2_exaggerated'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ],
      "source": [
        "data_train_imputed_final.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "735faf6a-cc50-467d-9ece-fb1a3eff29fa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "735faf6a-cc50-467d-9ece-fb1a3eff29fa",
        "outputId": "4344a99c-a975-46c0-cc4f-b737285fe71f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are no missing values in the training set.\n"
          ]
        }
      ],
      "source": [
        "# Check for remaining missing values in the imputed training set\n",
        "missing_data_train = data_train_imputed_final.isnull().sum()\n",
        "missing_columns_train = missing_data_train[missing_data_train > 0]\n",
        "\n",
        "if missing_columns_train.empty:\n",
        "    print(\"There are no missing values in the training set.\")\n",
        "else:\n",
        "    print(\"Columns with remaining missing values in the training set:\\n\", missing_columns_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d7383e5f-4050-4e81-8f5b-1b926eff78e9",
      "metadata": {
        "id": "d7383e5f-4050-4e81-8f5b-1b926eff78e9"
      },
      "source": [
        "## Encoding\n",
        "\n",
        "Since the machine learning model in the background models and finds patterns in our data. It only supports numeric values. For this reason, categorical variables must be coded to numeric values.\n",
        "\n",
        "Categorical variables can be of 3 types:\n",
        "* Binary variables: Binary variables can be represented with two values, 1 and 0. Examples are whether or not the variable belongs to a group.\n",
        "* Ordinal variables: Ordinal variables are a type of variables that have a specific order and can be represented with numeric variables through a label encoder. An example is High, Medium, and Low which can be represented as 3, 2, 1.\n",
        "* Nominal variables: Nominal variables are categorical variables that do not have a defined order, for these variables it is not recommended to use a label encoder, it is better to use one hot encoder in these cases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "42494eec-afbe-4d0c-9ce5-d48cd685171c",
      "metadata": {
        "id": "42494eec-afbe-4d0c-9ce5-d48cd685171c"
      },
      "outputs": [],
      "source": [
        "unique_values = data_train_imputed_final.apply(lambda x: x.unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "307070b0-ec32-461f-9633-e11e8a596bd5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "307070b0-ec32-461f-9633-e11e8a596bd5",
        "outputId": "4b73b42c-e2cb-407b-b66f-9aa664126fbc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "source_db                                             [eicu, mimic_iv, mimic_iii]\n",
              "comorbidity_score_value         [1.0, 3.0, 4.0, 0.0, 5.0, 2.0, 8.0, 6.0, 26.0,...\n",
              "unique_subject_id               [11633, 2049, 12464, 22496, 18805, 42892, 2164...\n",
              "unique_hospital_admission_id    [12807, 2209, 13681, 24674, 20590, 47032, 2374...\n",
              "unique_icustay_id               [12807, 2209, 13681, 24674, 20590, 47032, 2374...\n",
              "admission_age                   [51.0, 52.0, 79.0, 67.0, 41.0, 77.0, 65.0, 80....\n",
              "sex_female                                                                 [1, 0]\n",
              "race_ethnicity                          [Asian, White, Black, Hispanic OR Latino]\n",
              "BMI_admission                   [29.77777777777778, 41.96523053665911, 29.2184...\n",
              "weight_admission                [67.0, 104.1, 100.0, 102.5, 63.5, 66.0, 64.0, ...\n",
              "height_admission                [150.0, 157.5, 185.0, 170.0, 154.9, 162.6, 157...\n",
              "sofa_past_overall_24hr          [10.0, 3.0, 4.0, 6.0, 5.0, 12.0, 9.0, 0.0, 1.0...\n",
              "sofa_future_overall_24hr        [8.0, 9.0, 2.0, 7.0, 11.0, 5.0, 14.0, 3.0, 1.0...\n",
              "vitals_heart_rate               [89.0, 79.0, 92.0, 88.0, 72.0, 112.0, 76.0, 68...\n",
              "vitals_mbp_ni                   [79.0, 83.0, 77.0, 99.0, 68.0, 30.0, 63.0, 64....\n",
              "vitals_resp_rate                [24.0, 14.0, 15.0, 32.0, 34.0, 26.0, 12.0, 20....\n",
              "vitals_tempc                    [37.2, 34.4472, 36.4, 36.8, 35.6, 36.2, 38.5, ...\n",
              "cbc_wbc                         [26.4, 16.9, 12.2, 1.95, 10.06, 6.3, 26.15, 11...\n",
              "cbc_hematocrit                  [32.7, 33.4, 25.0, 27.2, 32.6, 28.7, 37.7, 37....\n",
              "cbc_platelet                    [182.0, 298.0, 187.0, 60.0, 122.0, 42.0, 76.0,...\n",
              "coag_inr                        [1.4, 1.1, 1.33, 1.2, 1.5, 1.6, 1.07, 1.03, 1....\n",
              "bmp_sodium                      [144.0, 141.0, 133.0, 126.0, 139.0, 136.0, 169...\n",
              "bmp_potassium                   [4.6, 3.7, 4.2, 3.9, 4.9, 4.7, 3.2, 3.6, 4.0, ...\n",
              "bmp_chloride                    [102.0, 107.0, 105.0, 88.0, 104.0, 101.0, 126....\n",
              "bmp_bicarbonate                 [19.0, 24.0, 21.0, 23.0, 27.0, 26.0, 28.0, 25....\n",
              "bmp_bun                         [31.0, 13.0, 21.0, 48.0, 50.0, 42.0, 140.0, 78...\n",
              "bmp_creatinine                  [4.2, 0.7, 1.07, 1.95, 0.81, 1.4, 2.4, 1.28, 3...\n",
              "bmp_glucose                     [271.0, 148.0, 137.0, 118.0, 69.0, 153.0, 269....\n",
              "bmp_aniongap                    [23.0, 10.0, 11.0, 21.0, 17.0, 15.0, 5.0, 7.0,...\n",
              "bmp_calcium                     [8.2, 7.6, 8.3, 8.1, 9.1, 9.7, 8.7, 7.8, 8.9, ...\n",
              "bmp_lactate                     [10.9, 1.8, 1.23, 3.6, 0.5, 1.6, 6.5, 1.3, 1.9...\n",
              "hfp_alt                         [131.0, 13.0, 23.0, 49.0, 16.0, 11.0, 39.0, 32...\n",
              "hfp_alp                         [221.0, 71.0, 45.0, 107.0, 70.0, 112.0, 69.0, ...\n",
              "hfp_ast                         [373.0, 17.0, 21.0, 89.0, 22.0, 23.0, 69.0, 31...\n",
              "hfp_bilirubin_total             [1.6, 0.5, 0.9, 1.2, 0.2, 6.0, 0.8, 0.7, 1.1, ...\n",
              "hfp_albumin                     [2.6, 3.7, 3.8, 3.0, 4.2, 3.1, 2.3, 3.4, 2.9, ...\n",
              "others_ck_cpk                   [77.0, 119.0, 114.5, 348.0, 164.0, 214.0, 82.0...\n",
              "in_hospital_mortality                                                  [0.0, 1.0]\n",
              "pH                              [7.43, 7.38, 7.34, 7.45, 7.32, 7.33, 7.24, 7.3...\n",
              "SpO2                            [97.0, 100.0, 98.0, 99.0, 90.0, 96.0, 94.0, 93...\n",
              "SaO2                            [99.0, 97.1, 98.1, 89.6, 96.0, 97.0, 88.0, 97....\n",
              "sex                                                                        [F, M]\n",
              "age_group                       ['<65', '>=65']\n",
              "Categories (2, object): ['<65'...\n",
              "hidden_hypoxemia                                                    [False, True]\n",
              "SpO2_exaggerated                [97.0, 100.0, 98.0, 99.0, 90.0, 96.0, 94.0, 93...\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ],
      "source": [
        "unique_values"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c7d8887-e43e-4ab5-a6f7-3312ed37930c",
      "metadata": {
        "id": "3c7d8887-e43e-4ab5-a6f7-3312ed37930c"
      },
      "source": [
        "## Standardization\n",
        "- Why Standardization?\n",
        "Standardization is generally preferred when dealing with datasets that contain outliers, features with different scales, or when the model assumptions align better with standardized data. It helps to reduce the influence of outliers, stabilize model coefficients, and improve the performance and interpretability of many machine learning algorithms. Normalization, on the other hand, is useful when the absolute scale of the features is important or when using algorithms that are sensitive to the range of the data.\n",
        "\n",
        "- Remember:\n",
        "    * When features are not on similar scales, some algorithms may be more heavily influenced by certain features than others, which can lead to suboptimal performance. Additionally, some algorithms (such as those based on distance calculations) can be sensitive to differences in scale between features, which can lead to incorrect results.\n",
        "    * By standardizing the data, we can ensure that each feature contributes equally to the model, regardless of its scale. This can lead to better accuracy and more robust models.\n",
        "\n",
        "\n",
        "\n",
        "Note: Use the function to normalize the data. Remember to exclude information that does not contribute to the final model, such as identifiers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8ea23eca-c62b-4671-85bf-5b67792f8f9e",
      "metadata": {
        "id": "8ea23eca-c62b-4671-85bf-5b67792f8f9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "567907ae-c47c-4818-8e85-470518bc141c"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['age_group', 'sex'] not found in axis\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-199-0ecef136f869>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_train_imputed_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_train_imputed_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'age_group'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sex'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5256\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5257\u001b[0m         \"\"\"\n\u001b[0;32m-> 5258\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5259\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5260\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4547\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4548\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4549\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4589\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4590\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4591\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4592\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6697\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6698\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6699\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{list(labels[mask])} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6700\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6701\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['age_group', 'sex'] not found in axis\""
          ]
        }
      ],
      "source": [
        "data_train_imputed_final = data_train_imputed_final.drop(['age_group', 'sex'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eef34f34-a342-48b8-9696-d319ec4efe51",
      "metadata": {
        "id": "eef34f34-a342-48b8-9696-d319ec4efe51"
      },
      "outputs": [],
      "source": [
        "# Define a function standardized_data\n",
        "\n",
        "def standardized_data(train_data, test_data=None, scaler=None, ignore_cols=[]):\n",
        "\n",
        "  # Please add your code here ...\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8f73ba15-ee82-4a77-bb83-a9ec89ee015c",
      "metadata": {
        "id": "8f73ba15-ee82-4a77-bb83-a9ec89ee015c"
      },
      "source": [
        "Note: Why Return the Scaler? Returning the scaler allows you to use the same scaler for any future data transformations, ensuring consistency. This is especially important if you need to apply the same scaling to new data or to reverse the scaling transformation.\n",
        "\n",
        "How to know what columns to include? You should exclude identifiers and categorical variables that are not intended to be scaled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4f46a09b-d1f2-4142-9ef8-405d0fc57dff",
      "metadata": {
        "id": "4f46a09b-d1f2-4142-9ef8-405d0fc57dff"
      },
      "outputs": [],
      "source": [
        "# Define the columns to ignore during normalization (identifiers and categorical variables)\n",
        "ignore_cols = [\n",
        "    'unique_subject_id', 'unique_hospital_admission_id', 'unique_icustay_id',\n",
        "    'source_db', 'sex_female', 'race_ethnicity', 'in_hospital_mortality', 'hidden_hypoxemia'\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0fec9fc5-07bd-4341-8af2-c0c8919465ef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fec9fc5-07bd-4341-8af2-c0c8919465ef",
        "outputId": "22a8533a-683a-4cdb-9c76-659fba243ed8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final standardized train shape: (36111, 43)\n",
            "Final standardized test shape: (9028, 43)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "final_standardized_train, final_standardized_test, scaler = standardized_data(\n",
        "    data_train_imputed_final, data_test_imputed_final, ignore_cols=ignore_cols\n",
        ")\n",
        "\n",
        "# Selecting ignored columns\n",
        "ignored_columns_train = data_train_imputed_final[ignore_cols]\n",
        "ignored_columns_test = data_test_imputed_final[ignore_cols]\n",
        "\n",
        "# Reset indices to ensure alignment before concatenation\n",
        "ignored_columns_train = ignored_columns_train.reset_index(drop=True)\n",
        "final_standardized_train = final_standardized_train.reset_index(drop=True)\n",
        "\n",
        "ignored_columns_test = ignored_columns_test.reset_index(drop=True)\n",
        "final_standardized_test = final_standardized_test.reset_index(drop=True)\n",
        "\n",
        "# Merge the ignored columns with the standardized columns\n",
        "final_standardized_train = pd.concat([ignored_columns_train, final_standardized_train], axis=1)\n",
        "final_standardized_test = pd.concat([ignored_columns_test, final_standardized_test], axis=1)\n",
        "\n",
        "# Display the shapes of the DataFrames for verification\n",
        "print(\"Final standardized train shape:\", final_standardized_train.shape)\n",
        "print(\"Final standardized test shape:\", final_standardized_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4a16e7d8-423d-4f22-aa10-d6b8ba6db529",
      "metadata": {
        "id": "4a16e7d8-423d-4f22-aa10-d6b8ba6db529"
      },
      "source": [
        "## Defining our X_train, y_train, X_test, y_test\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train_imputed_final.groupby(\"race_ethnicity\")['in_hospital_mortality'].describe() # to see how many races in our X train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NqntuoKRKkAp",
        "outputId": "23237742-ff6a-4036-d398-d9c4ddd46f01"
      },
      "id": "NqntuoKRKkAp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      count      mean       std  min  25%  50%  75%  max\n",
              "race_ethnicity                                                          \n",
              "Asian                 700.0  0.180000  0.384462  0.0  0.0  0.0  0.0  1.0\n",
              "Black                3860.0  0.171503  0.376996  0.0  0.0  0.0  0.0  1.0\n",
              "Hispanic OR Latino   1689.0  0.182948  0.386739  0.0  0.0  0.0  0.0  1.0\n",
              "White               29862.0  0.172828  0.378105  0.0  0.0  0.0  0.0  1.0"
            ],
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              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "summary": "{\n  \"name\": \"data_train_imputed_final\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"race_ethnicity\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Black\",\n          \"White\",\n          \"Asian\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13952.06418121228,\n        \"min\": 700.0,\n        \"max\": 29862.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3860.0,\n          29862.0,\n          700.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005534104994622168,\n        \"min\": 0.17150259067357512,\n        \"max\": 0.18294849023090587,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.17150259067357512,\n          0.17282834371441966,\n          0.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004761098586618935,\n        \"min\": 0.37699638240125927,\n        \"max\": 0.3867387925618351,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.37699638240125927,\n          0.37810513722448774,\n          0.38446216822762064\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "45835b9d-05bb-43db-b7cb-0a7593f04daa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45835b9d-05bb-43db-b7cb-0a7593f04daa",
        "outputId": "5a08ee9b-4b9d-4790-ab7c-c02e3244d964"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of X_train: 36111\n",
            "Length of y_train: 36111\n",
            "X_train indices: RangeIndex(start=0, stop=36111, step=1)\n",
            "y_train indices: RangeIndex(start=0, stop=36111, step=1)\n"
          ]
        }
      ],
      "source": [
        "# List of columns to exclude from the feature matrix\n",
        "exclude_cols = [\n",
        "    'sofa_future_overall_24hr', 'sofa_past_overall_24hr',\n",
        "    'unique_subject_id', 'unique_hospital_admission_id',\n",
        "    'unique_icustay_id', 'source_db', 'race_ethnicity', 'in_hospital_mortality'\n",
        "]\n",
        "\n",
        "# Define the feature matrix X and target variable y for training and test sets\n",
        "X_train = final_standardized_train.drop(exclude_cols, axis=1)\n",
        "y_train = final_standardized_train['in_hospital_mortality']\n",
        "\n",
        "X_test = final_standardized_test.drop(exclude_cols, axis=1)\n",
        "y_test = final_standardized_test['in_hospital_mortality']\n",
        "\n",
        "# Check lengths and indices\n",
        "print(f\"Length of X_train: {len(X_train)}\")\n",
        "print(f\"Length of y_train: {len(y_train)}\")\n",
        "print(f\"X_train indices: {X_train.index}\")\n",
        "print(f\"y_train indices: {y_train.index}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f92fe25-5836-4140-8d2e-a63895e2380b",
      "metadata": {
        "id": "0f92fe25-5836-4140-8d2e-a63895e2380b"
      },
      "source": [
        "## Step 1: Understanding the influence of hidden hypoxemia on patient outcomes\n",
        "* Following the paper's approach, we are going to perform univariate and multivariate logistic regression to assess binary endpoints (e.g., in-hospital mortality) while adjusting for other covariates (e.g., age, sex, SOFA score), and to use analysis of variance to test for the impact of hidden hypoxemia.\n",
        "\n",
        "* Goal: Assess the impact of hidden hypoxemia by analyzing its effect on patient outcomes like in-hospital mortality."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Univariate Logistic Regression"
      ],
      "metadata": {
        "id": "AXmJ1W2ZvtZl"
      },
      "id": "AXmJ1W2ZvtZl"
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm  # Add statsmodels for logistic regression\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# List of variables to analyze\n",
        "vars = ['admission_age',\n",
        "        'sex_female',\n",
        "        'BMI_admission',\n",
        "        'vitals_heart_rate', 'vitals_mbp_ni',\n",
        "        'vitals_resp_rate', 'vitals_tempc',\n",
        "        'cbc_wbc', 'cbc_hematocrit',\n",
        "        'cbc_platelet', 'coag_inr', 'bmp_sodium',\n",
        "        'bmp_potassium', 'bmp_chloride', 'bmp_bicarbonate', 'bmp_bun',\n",
        "        'bmp_creatinine', 'bmp_glucose', 'bmp_aniongap', 'bmp_calcium',\n",
        "        'bmp_lactate', 'hfp_alt', 'hfp_alp', 'hfp_ast', 'hfp_bilirubin_total',\n",
        "        'hfp_albumin', 'others_ck_cpk',\n",
        "        'SpO2', 'SaO2', 'pH',\n",
        "        'SpO2_exaggerated', 'hidden_hypoxemia']\n",
        "\n",
        "# Initialize an empty DataFrame to store the results\n",
        "results_df = pd.DataFrame()\n",
        "\n",
        "X_train['hidden_hypoxemia'] = X_train['hidden_hypoxemia'].astype(int)\n",
        "\n",
        "# Iterate over each variable in the list\n",
        "for var in vars:\n",
        "    X = X_train[var]  # Select the current variable from the training data\n",
        "    y = y_train       # Target variable remains the same\n",
        "\n",
        "    # statsmodels must explicitly add a constant term\n",
        "    # Add a constant term to the independent variable for the logistic regression model\n",
        "    X = sm.add_constant(X)\n",
        "\n",
        "    # Application of logistic regression model using statsmodels\n",
        "    model = sm.Logit(y, X)\n",
        "    result = model.fit(disp=False)  # Fit the model without printing the summary\n",
        "\n",
        "    # Calculation of p-values and odds ratios\n",
        "    # Extract the summary of the fitted model\n",
        "    summary = result.summary2()\n",
        "\n",
        "    # Extract p-values from the summary\n",
        "    p_values = summary.tables[1]['P>|z|']\n",
        "\n",
        "    # Calculate the odds ratios by exponentiating the coefficients\n",
        "    odds_ratios = np.exp(summary.tables[1]['Coef.'])\n",
        "\n",
        "    # Calculate the 95% confidence intervals for the odds ratios\n",
        "    conf_int = np.exp(summary.tables[1][['[0.025', '0.975]']])\n",
        "\n",
        "    # Create a temporary DataFrame to store the results for the current variable\n",
        "    results_df_ = pd.DataFrame({\n",
        "        'Variable': summary.tables[1].index,  # Variable names\n",
        "        'P-value': p_values,                  # P-values\n",
        "        'Odds Ratio': odds_ratios,            # Odds ratios\n",
        "        '95% CI (Lower)': conf_int['[0.025'], # Lower bound of the 95% confidence interval\n",
        "        '95% CI (Upper)': conf_int['0.975]']  # Upper bound of the 95% confidence interval\n",
        "    })\n",
        "\n",
        "    # Filter the results to include only the current variable and concatenate to the final results DataFrame\n",
        "    results_df = pd.concat([results_df, results_df_[results_df_['Variable'] == var]], axis=0)\n",
        "\n",
        "# Display the final results DataFrame\n",
        "display(results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QpJjAie-wOwx",
        "outputId": "ea1e2acb-b97c-4bbb-8ce6-4aa2ab341c5c"
      },
      "id": "QpJjAie-wOwx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                Variable        P-value    Odds Ratio  \\\n",
              "admission_age              admission_age  7.414620e-107  1.398997e+00   \n",
              "sex_female                    sex_female   2.916485e-01  1.029952e+00   \n",
              "BMI_admission              BMI_admission   7.580564e-01  9.952314e-01   \n",
              "vitals_heart_rate      vitals_heart_rate   1.716861e-89  1.309789e+00   \n",
              "vitals_mbp_ni              vitals_mbp_ni   1.865289e-31  8.418927e-01   \n",
              "vitals_resp_rate        vitals_resp_rate  4.731161e-160  1.416930e+00   \n",
              "vitals_tempc                vitals_tempc   5.384142e-64  7.999910e-01   \n",
              "cbc_wbc                          cbc_wbc   1.706024e-47  1.202974e+00   \n",
              "cbc_hematocrit            cbc_hematocrit   8.662029e-13  9.039575e-01   \n",
              "cbc_platelet                cbc_platelet   1.878204e-08  9.209094e-01   \n",
              "coag_inr                        coag_inr   2.229844e-98  1.294575e+00   \n",
              "bmp_sodium                    bmp_sodium   8.277146e-01  1.003031e+00   \n",
              "bmp_potassium              bmp_potassium   2.628940e-29  1.161710e+00   \n",
              "bmp_chloride                bmp_chloride   3.081824e-08  9.264498e-01   \n",
              "bmp_bicarbonate          bmp_bicarbonate  2.547330e-108  7.222670e-01   \n",
              "bmp_bun                          bmp_bun  7.877076e-182  1.414951e+00   \n",
              "bmp_creatinine            bmp_creatinine   6.609697e-63  1.218042e+00   \n",
              "bmp_glucose                  bmp_glucose   2.098366e-33  1.157901e+00   \n",
              "bmp_aniongap                bmp_aniongap  2.419686e-173  1.432712e+00   \n",
              "bmp_calcium                  bmp_calcium   2.480618e-04  9.522822e-01   \n",
              "bmp_lactate                  bmp_lactate  1.334952e-291  1.609341e+00   \n",
              "hfp_alt                          hfp_alt   2.555270e-44  1.202696e+00   \n",
              "hfp_alp                          hfp_alp   9.741564e-75  1.271915e+00   \n",
              "hfp_ast                          hfp_ast   1.924241e-43  1.247718e+00   \n",
              "hfp_bilirubin_total  hfp_bilirubin_total   5.116293e-74  1.304696e+00   \n",
              "hfp_albumin                  hfp_albumin  7.519897e-237  6.276498e-01   \n",
              "others_ck_cpk              others_ck_cpk   2.436093e-01  1.013409e+00   \n",
              "SpO2                                SpO2  1.681449e-121  7.528887e-01   \n",
              "SaO2                                SaO2   7.177579e-45  8.385254e-01   \n",
              "pH                                    pH  2.524439e-164  5.843733e-63   \n",
              "SpO2_exaggerated        SpO2_exaggerated  1.249468e-111  7.619580e-01   \n",
              "hidden_hypoxemia        hidden_hypoxemia   2.246503e-21  1.842978e+00   \n",
              "\n",
              "                     95% CI (Lower)  95% CI (Upper)  \n",
              "admission_age          1.357690e+00    1.441561e+00  \n",
              "sex_female             9.749775e-01    1.088027e+00  \n",
              "BMI_admission          9.654175e-01    1.025966e+00  \n",
              "vitals_heart_rate      1.275702e+00    1.344788e+00  \n",
              "vitals_mbp_ni          8.179018e-01    8.665873e-01  \n",
              "vitals_resp_rate       1.381479e+00    1.453291e+00  \n",
              "vitals_tempc           7.795401e-01    8.209785e-01  \n",
              "cbc_wbc                1.173250e+00    1.233452e+00  \n",
              "cbc_hematocrit         8.792810e-01    9.293265e-01  \n",
              "cbc_platelet           8.948371e-01    9.477414e-01  \n",
              "coag_inr               1.263827e+00    1.326071e+00  \n",
              "bmp_sodium             9.760649e-01    1.030741e+00  \n",
              "bmp_potassium          1.131736e+00    1.192477e+00  \n",
              "bmp_chloride           9.017311e-01    9.518461e-01  \n",
              "bmp_bicarbonate        7.017328e-01    7.434022e-01  \n",
              "bmp_bun                1.381869e+00    1.448826e+00  \n",
              "bmp_creatinine         1.190237e+00    1.246498e+00  \n",
              "bmp_glucose            1.130601e+00    1.185860e+00  \n",
              "bmp_aniongap           1.397186e+00    1.469140e+00  \n",
              "bmp_calcium            9.277003e-01    9.775155e-01  \n",
              "bmp_lactate            1.568737e+00    1.650997e+00  \n",
              "hfp_alt                1.171942e+00    1.234258e+00  \n",
              "hfp_alp                1.239553e+00    1.305122e+00  \n",
              "hfp_ast                1.209164e+00    1.287501e+00  \n",
              "hfp_bilirubin_total    1.267857e+00    1.342605e+00  \n",
              "hfp_albumin            6.104541e-01    6.453298e-01  \n",
              "others_ck_cpk          9.909714e-01    1.036355e+00  \n",
              "SpO2                   7.352302e-01    7.709712e-01  \n",
              "SaO2                   8.181832e-01    8.593733e-01  \n",
              "pH                     2.003750e-67    1.704265e-58  \n",
              "SpO2_exaggerated       7.440869e-01    7.802583e-01  \n",
              "hidden_hypoxemia       1.624423e+00    2.090937e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10bf2227-3ee1-4280-a97a-5e88cb4e4c4b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>P-value</th>\n",
              "      <th>Odds Ratio</th>\n",
              "      <th>95% CI (Lower)</th>\n",
              "      <th>95% CI (Upper)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>admission_age</th>\n",
              "      <td>admission_age</td>\n",
              "      <td>7.414620e-107</td>\n",
              "      <td>1.398997e+00</td>\n",
              "      <td>1.357690e+00</td>\n",
              "      <td>1.441561e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex_female</th>\n",
              "      <td>sex_female</td>\n",
              "      <td>2.916485e-01</td>\n",
              "      <td>1.029952e+00</td>\n",
              "      <td>9.749775e-01</td>\n",
              "      <td>1.088027e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI_admission</th>\n",
              "      <td>BMI_admission</td>\n",
              "      <td>7.580564e-01</td>\n",
              "      <td>9.952314e-01</td>\n",
              "      <td>9.654175e-01</td>\n",
              "      <td>1.025966e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vitals_heart_rate</th>\n",
              "      <td>vitals_heart_rate</td>\n",
              "      <td>1.716861e-89</td>\n",
              "      <td>1.309789e+00</td>\n",
              "      <td>1.275702e+00</td>\n",
              "      <td>1.344788e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vitals_mbp_ni</th>\n",
              "      <td>vitals_mbp_ni</td>\n",
              "      <td>1.865289e-31</td>\n",
              "      <td>8.418927e-01</td>\n",
              "      <td>8.179018e-01</td>\n",
              "      <td>8.665873e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vitals_resp_rate</th>\n",
              "      <td>vitals_resp_rate</td>\n",
              "      <td>4.731161e-160</td>\n",
              "      <td>1.416930e+00</td>\n",
              "      <td>1.381479e+00</td>\n",
              "      <td>1.453291e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vitals_tempc</th>\n",
              "      <td>vitals_tempc</td>\n",
              "      <td>5.384142e-64</td>\n",
              "      <td>7.999910e-01</td>\n",
              "      <td>7.795401e-01</td>\n",
              "      <td>8.209785e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cbc_wbc</th>\n",
              "      <td>cbc_wbc</td>\n",
              "      <td>1.706024e-47</td>\n",
              "      <td>1.202974e+00</td>\n",
              "      <td>1.173250e+00</td>\n",
              "      <td>1.233452e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cbc_hematocrit</th>\n",
              "      <td>cbc_hematocrit</td>\n",
              "      <td>8.662029e-13</td>\n",
              "      <td>9.039575e-01</td>\n",
              "      <td>8.792810e-01</td>\n",
              "      <td>9.293265e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cbc_platelet</th>\n",
              "      <td>cbc_platelet</td>\n",
              "      <td>1.878204e-08</td>\n",
              "      <td>9.209094e-01</td>\n",
              "      <td>8.948371e-01</td>\n",
              "      <td>9.477414e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>coag_inr</th>\n",
              "      <td>coag_inr</td>\n",
              "      <td>2.229844e-98</td>\n",
              "      <td>1.294575e+00</td>\n",
              "      <td>1.263827e+00</td>\n",
              "      <td>1.326071e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_sodium</th>\n",
              "      <td>bmp_sodium</td>\n",
              "      <td>8.277146e-01</td>\n",
              "      <td>1.003031e+00</td>\n",
              "      <td>9.760649e-01</td>\n",
              "      <td>1.030741e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_potassium</th>\n",
              "      <td>bmp_potassium</td>\n",
              "      <td>2.628940e-29</td>\n",
              "      <td>1.161710e+00</td>\n",
              "      <td>1.131736e+00</td>\n",
              "      <td>1.192477e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_chloride</th>\n",
              "      <td>bmp_chloride</td>\n",
              "      <td>3.081824e-08</td>\n",
              "      <td>9.264498e-01</td>\n",
              "      <td>9.017311e-01</td>\n",
              "      <td>9.518461e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_bicarbonate</th>\n",
              "      <td>bmp_bicarbonate</td>\n",
              "      <td>2.547330e-108</td>\n",
              "      <td>7.222670e-01</td>\n",
              "      <td>7.017328e-01</td>\n",
              "      <td>7.434022e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_bun</th>\n",
              "      <td>bmp_bun</td>\n",
              "      <td>7.877076e-182</td>\n",
              "      <td>1.414951e+00</td>\n",
              "      <td>1.381869e+00</td>\n",
              "      <td>1.448826e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_creatinine</th>\n",
              "      <td>bmp_creatinine</td>\n",
              "      <td>6.609697e-63</td>\n",
              "      <td>1.218042e+00</td>\n",
              "      <td>1.190237e+00</td>\n",
              "      <td>1.246498e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_glucose</th>\n",
              "      <td>bmp_glucose</td>\n",
              "      <td>2.098366e-33</td>\n",
              "      <td>1.157901e+00</td>\n",
              "      <td>1.130601e+00</td>\n",
              "      <td>1.185860e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_aniongap</th>\n",
              "      <td>bmp_aniongap</td>\n",
              "      <td>2.419686e-173</td>\n",
              "      <td>1.432712e+00</td>\n",
              "      <td>1.397186e+00</td>\n",
              "      <td>1.469140e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_calcium</th>\n",
              "      <td>bmp_calcium</td>\n",
              "      <td>2.480618e-04</td>\n",
              "      <td>9.522822e-01</td>\n",
              "      <td>9.277003e-01</td>\n",
              "      <td>9.775155e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmp_lactate</th>\n",
              "      <td>bmp_lactate</td>\n",
              "      <td>1.334952e-291</td>\n",
              "      <td>1.609341e+00</td>\n",
              "      <td>1.568737e+00</td>\n",
              "      <td>1.650997e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hfp_alt</th>\n",
              "      <td>hfp_alt</td>\n",
              "      <td>2.555270e-44</td>\n",
              "      <td>1.202696e+00</td>\n",
              "      <td>1.171942e+00</td>\n",
              "      <td>1.234258e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hfp_alp</th>\n",
              "      <td>hfp_alp</td>\n",
              "      <td>9.741564e-75</td>\n",
              "      <td>1.271915e+00</td>\n",
              "      <td>1.239553e+00</td>\n",
              "      <td>1.305122e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hfp_ast</th>\n",
              "      <td>hfp_ast</td>\n",
              "      <td>1.924241e-43</td>\n",
              "      <td>1.247718e+00</td>\n",
              "      <td>1.209164e+00</td>\n",
              "      <td>1.287501e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hfp_bilirubin_total</th>\n",
              "      <td>hfp_bilirubin_total</td>\n",
              "      <td>5.116293e-74</td>\n",
              "      <td>1.304696e+00</td>\n",
              "      <td>1.267857e+00</td>\n",
              "      <td>1.342605e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hfp_albumin</th>\n",
              "      <td>hfp_albumin</td>\n",
              "      <td>7.519897e-237</td>\n",
              "      <td>6.276498e-01</td>\n",
              "      <td>6.104541e-01</td>\n",
              "      <td>6.453298e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>others_ck_cpk</th>\n",
              "      <td>others_ck_cpk</td>\n",
              "      <td>2.436093e-01</td>\n",
              "      <td>1.013409e+00</td>\n",
              "      <td>9.909714e-01</td>\n",
              "      <td>1.036355e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpO2</th>\n",
              "      <td>SpO2</td>\n",
              "      <td>1.681449e-121</td>\n",
              "      <td>7.528887e-01</td>\n",
              "      <td>7.352302e-01</td>\n",
              "      <td>7.709712e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SaO2</th>\n",
              "      <td>SaO2</td>\n",
              "      <td>7.177579e-45</td>\n",
              "      <td>8.385254e-01</td>\n",
              "      <td>8.181832e-01</td>\n",
              "      <td>8.593733e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>pH</td>\n",
              "      <td>2.524439e-164</td>\n",
              "      <td>5.843733e-63</td>\n",
              "      <td>2.003750e-67</td>\n",
              "      <td>1.704265e-58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SpO2_exaggerated</th>\n",
              "      <td>SpO2_exaggerated</td>\n",
              "      <td>1.249468e-111</td>\n",
              "      <td>7.619580e-01</td>\n",
              "      <td>7.440869e-01</td>\n",
              "      <td>7.802583e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hidden_hypoxemia</th>\n",
              "      <td>hidden_hypoxemia</td>\n",
              "      <td>2.246503e-21</td>\n",
              "      <td>1.842978e+00</td>\n",
              "      <td>1.624423e+00</td>\n",
              "      <td>2.090937e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9bcfee2a-0d3a-4616-b2dd-9200cfa36a85 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_df",
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 32,\n  \"fields\": [\n    {\n      \"column\": \"Variable\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"pH\",\n          \"bmp_bun\",\n          \"hfp_bilirubin_total\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P-value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20188995405439403,\n        \"min\": 1.3349516341812661e-291,\n        \"max\": 0.8277146297074276,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          2.524439091240986e-164,\n          7.877075863405785e-182,\n          5.116293143758258e-74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Odds Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34112117754572185,\n        \"min\": 5.843732836712979e-63,\n        \"max\": 1.8429777187012732,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          5.843732836712979e-63,\n          1.4149512017907666,\n          1.3046960244404904\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"95% CI (Lower)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3215110318601166,\n        \"min\": 2.0037496648585154e-67,\n        \"max\": 1.6244234575204886,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          2.0037496648585154e-67,\n          1.3818686030498015,\n          1.267857442821526\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"95% CI (Upper)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3656616330831984,\n        \"min\": 1.7042654611891742e-58,\n        \"max\": 2.0909368526442305,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          1.7042654611891742e-58,\n          1.4488258138512615,\n          1.3426049796281716\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a figure and set the size\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Create a horizontal bar plot of the p-values for each variable\n",
        "plt.barh(results_df['Variable'], results_df[\"P-value\"], label='Exponential Growth')  # Draw a horizontal bar graph\n",
        "\n",
        "# Set the x-axis to a logarithmic scale\n",
        "plt.xscale('log')  # Set the x-axis to a logarithmic scale\n",
        "plt.gca().invert_yaxis()  # Invert the order of the y-axis (change from top to bottom)\n",
        "\n",
        "# Decorate the graph\n",
        "plt.title('Exponential p_value on Log Scale')  # Set the title of the graph\n",
        "plt.xlabel('X-axis (log scale)')  # Change the label of the x-axis\n",
        "plt.ylabel('Y-axis')  # Set the label of the y-axis\n",
        "plt.grid(True)  # Enable grid\n",
        "plt.legend()  # Display the legend\n",
        "\n",
        "# Display the graph\n",
        "plt.show()  # Show the plot\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "MPs3X8-Tzzoy",
        "outputId": "ba7fd5ef-2129-47e7-e23d-b3be3473dae3"
      },
      "id": "MPs3X8-Tzzoy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: What key insights can you derive from this figure?\n",
        "- Your answer: ....\n"
      ],
      "metadata": {
        "id": "5ZpRXubmE_P7"
      },
      "id": "5ZpRXubmE_P7"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Multivariate Logistic Regression"
      ],
      "metadata": {
        "id": "nXdm4PAZwSXf"
      },
      "id": "nXdm4PAZwSXf"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8323e33e-2802-41bf-b495-710467233d07",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8323e33e-2802-41bf-b495-710467233d07",
        "outputId": "0a9fb4d7-1ccb-44b9-c191-149a20b2006b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.85      0.98      0.91      7434\n",
            "         1.0       0.67      0.19      0.29      1594\n",
            "\n",
            "    accuracy                           0.84      9028\n",
            "   macro avg       0.76      0.58      0.60      9028\n",
            "weighted avg       0.82      0.84      0.80      9028\n",
            "\n",
            "Accuracy: 0.84\n",
            "AUC: 0.78\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report, roc_auc_score\n",
        "\n",
        "# Define the features to include\n",
        "features_to_include = ['admission_age',\n",
        "        'sex_female',\n",
        "        'BMI_admission',\n",
        "        'vitals_heart_rate', 'vitals_mbp_ni',\n",
        "        'vitals_resp_rate', 'vitals_tempc',\n",
        "        'cbc_wbc', 'cbc_hematocrit',\n",
        "        'cbc_platelet', 'coag_inr', 'bmp_sodium',\n",
        "        'bmp_potassium', 'bmp_chloride', 'bmp_bicarbonate', 'bmp_bun',\n",
        "        'bmp_creatinine', 'bmp_glucose', 'bmp_aniongap', 'bmp_calcium',\n",
        "        'bmp_lactate', 'hfp_alt', 'hfp_alp', 'hfp_ast', 'hfp_bilirubin_total',\n",
        "        'hfp_albumin', 'others_ck_cpk',\n",
        "        'pH', 'hidden_hypoxemia']\n",
        "\n",
        "# Fit the logistic regression model\n",
        "model = LogisticRegression (max_iter = 1000)\n",
        "result = model.fit(X_train[features_to_include], y_train)\n",
        "\n",
        "# Predictions\n",
        "y_pred = model.predict(X_test[features_to_include])\n",
        "y_pred_proba = model.predict_proba(X_test[features_to_include]) [:,1]\n",
        "\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Classification Report:\")\n",
        "print (classification_report(y_test, y_pred))\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred):.2f}\")\n",
        "\n",
        "# Area under the Curve (AUC)\n",
        "auc = roc_auc_score(y_test, y_pred_proba)\n",
        "print(f\"AUC: {auc:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a5db3438-2de3-46d8-ada0-6dd282c03888",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "a5db3438-2de3-46d8-ada0-6dd282c03888",
        "outputId": "2e648775-9010-427c-f360-b072c19a6876"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Extract coefficients and their importance\n",
        "coefficients = model.coef_[0]\n",
        "importance = pd.DataFrame({'Feature': X_train[features_to_include].columns, 'Importance': coefficients})\n",
        "importance = importance.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Importance', y='Feature', data=importance)\n",
        "plt.title('Feature Importance in Logistic Regression')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: What key insights can you derive from this figure?\n",
        "- Your answer: ....\n"
      ],
      "metadata": {
        "id": "eenSdBS7GQYG"
      },
      "id": "eenSdBS7GQYG"
    },
    {
      "cell_type": "markdown",
      "id": "b6707f53-3a7b-456f-800e-a5502750163c",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "id": "b6707f53-3a7b-456f-800e-a5502750163c"
      },
      "source": [
        "#### *Optional*\n",
        " * Task: We already assessed the importance of hidden hypoxemia on in-hospital mortality as a binary outcome, now try to:\n",
        "   - Assess the importance of hidden hypoxemia on ordinal outcomes, such as CVSOFA (Cardiovascular Sequential Organ Failure Assessment) and RSOFA (Respiratory Sequential Organ Failure Assessment) scores.\n",
        "   - Assess the importance of hidden hypoxemia on continuous outcomes, such as creatinine and lactate levels.\n",
        "  \n",
        " * What observations do you see?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d90f506-dbd7-4c2b-9fef-20945b45ccbe",
      "metadata": {
        "id": "9d90f506-dbd7-4c2b-9fef-20945b45ccbe"
      },
      "source": [
        "## Step 2: Predicting In Hospital Mortality using different features in the dataset\n",
        "Goal: Compare the performance of different models that include various combinations of features (SpO2, Sp02_exaggerated, SaO2) in predicting in-hospital mortality across different patient races. This step demonstrates how including or excluding certain features impacts model accuracy, fairness, and overall predictive performance, emphasizing the need for careful feature selection in clinical predictions.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0854d94-7e71-4217-91a1-78e0793c673e",
      "metadata": {
        "id": "a0854d94-7e71-4217-91a1-78e0793c673e"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Please add your code here ...\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "03c3bf68-1d1c-4ceb-9702-e57622c1a6b2",
      "metadata": {
        "id": "03c3bf68-1d1c-4ceb-9702-e57622c1a6b2"
      },
      "outputs": [],
      "source": [
        "# Define feature sets dynamically excluding 'hidden_hypoxemia'\n",
        "all_features = X_train.columns.tolist()\n",
        "\n",
        "# Exclude 'SpO2_exaggerated', 'SpO2', and 'hidden_hypoxemia' for features_sao2\n",
        "features_sao2 = [feature for feature in all_features if feature not in ['SpO2_exaggerated', 'SpO2', 'hidden_hypoxemia']]\n",
        "\n",
        "# Exclude 'SaO2', 'SpO2_exaggerated', and 'hidden_hypoxemia' for features_spo2\n",
        "features_spo2 = [feature for feature in all_features if feature not in ['SaO2', 'SpO2_exaggerated', 'hidden_hypoxemia']]\n",
        "\n",
        "# Exclude 'SaO2', 'SpO2', and 'hidden_hypoxemia' for features_exaggerated_spo2\n",
        "features_exaggerated_spo2 = [feature for feature in all_features if feature not in ['SaO2', 'SpO2', 'hidden_hypoxemia']]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c0b5dd2-aa04-4649-8cc1-1506cc570d30",
      "metadata": {
        "id": "2c0b5dd2-aa04-4649-8cc1-1506cc570d30"
      },
      "outputs": [],
      "source": [
        "# Train each model\n",
        "model_sao2, y_pred_sao2, y_pred_proba_sao2 = train_model(X_train, X_test, y_train, features_sao2)\n",
        "model_spo2, y_pred_spo2, y_pred_proba_spo2 = train_model(X_train, X_test, y_train, features_spo2)\n",
        "model_exaggerated_spo2, y_pred_exaggerated_spo2, y_pred_proba_exaggerated_spo2 = train_model(X_train, X_test, y_train, features_exaggerated_spo2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "76a5f110-3a9d-4d60-8878-b11e016ecaae",
      "metadata": {
        "id": "76a5f110-3a9d-4d60-8878-b11e016ecaae"
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      "source": [
        "from sklearn.metrics import roc_auc_score, accuracy_score, classification_report, balanced_accuracy_score\n",
        "\n",
        "# Evaluate across races\n",
        "def evaluate_across_races(X_test, y_test, y_pred, y_pred_proba, data_test, model, features):\n",
        "    \"\"\"\n",
        "    Evaluate model performance across different race/ethnicity groups.\n",
        "\n",
        "    This function evaluates the performance of a trained machine learning model across\n",
        "    different race/ethnicity groups in the test dataset. It generates classification reports,\n",
        "    calculates accuracy and AUC scores, and identifies feature importance for each group.\n",
        "\n",
        "    Parameters:\n",
        "    X_test (array-like): Test features.\n",
        "    y_test (array-like): True labels for the test set.\n",
        "    y_pred (array-like): Predicted labels for the test set.\n",
        "    y_pred_proba (array-like): Predicted probabilities for the positive class.\n",
        "    data_test (DataFrame): Test dataset containing the race/ethnicity information.\n",
        "    model (sklearn model): Trained machine learning model.\n",
        "    features (list): List of feature names.\n",
        "\n",
        "    Returns:\n",
        "    group_reports (dict): Dictionary containing classification reports, accuracy, and AUC scores for each race/ethnicity group.\n",
        "    feature_importance_dict (dict): Dictionary containing feature importance for each race/ethnicity group.\n",
        "    \"\"\"\n",
        "\n",
        "    # Create a DataFrame for evaluation using the test features\n",
        "    evaluation_df = pd.DataFrame(X_test, columns=features)\n",
        "\n",
        "    # Add the actual test labels to the DataFrame\n",
        "    evaluation_df['y_test'] = y_test.values\n",
        "\n",
        "    # Add the predicted labels to the DataFrame\n",
        "    evaluation_df['y_pred'] = y_pred\n",
        "\n",
        "    # Add the predicted probabilities to the DataFrame\n",
        "    evaluation_df['y_pred_proba'] = y_pred_proba\n",
        "\n",
        "    # Add the race/ethnicity information to the DataFrame\n",
        "    evaluation_df['race_ethnicity'] = data_test['race_ethnicity'].values\n",
        "\n",
        "\n",
        "\n",
        "    # Initialize dictionaries to store reports and feature importances for each race group\n",
        "    group_reports = pd.DataFrame()\n",
        "\n",
        "    # Loop through each unique race/ethnicity group in the evaluation DataFrame\n",
        "    for i, group in enumerate(evaluation_df['race_ethnicity'].unique()):\n",
        "        # Filter the data for the current race/ethnicity group\n",
        "        group_data = evaluation_df[evaluation_df['race_ethnicity'] == group]\n",
        "\n",
        "        # Extract the actual labels for the current group\n",
        "        y_test_group = group_data['y_test']\n",
        "\n",
        "        # Extract the predicted labels for the current group\n",
        "        y_pred_group = group_data['y_pred']\n",
        "\n",
        "        # Extract the predicted probabilities for the current group\n",
        "        y_pred_proba_group = group_data['y_pred_proba']\n",
        "\n",
        "\n",
        "        # Generate a classification report for the current group\n",
        "        report = classification_report(y_test_group, y_pred_group, output_dict=True)\n",
        "\n",
        "        group_reports.loc[i, 'race_ethnicity'] = group\n",
        "\n",
        "        # Weighted precision for the race group\n",
        "        group_reports.loc[i, \"Weighted Precision\"] = report['weighted avg']['precision']\n",
        "\n",
        "        # Weighted recall for the race group\n",
        "        group_reports.loc[i, \"Weighted Recall\"] = report['weighted avg']['recall']\n",
        "\n",
        "        # Weighted F1-score for the race group\n",
        "        group_reports.loc[i, \"Weighted F1-Score\"] = report['weighted avg']['f1-score']\n",
        "\n",
        "        # Calculate the balanced accuracy for the current group\n",
        "        group_reports.loc[i, 'balanced_accuracy'] = balanced_accuracy_score(y_test_group, y_pred_group)\n",
        "\n",
        "        # Calculate the AUC score for the current group\n",
        "        group_reports.loc[i, 'auc'] = roc_auc_score(y_test_group, y_pred_proba_group)\n",
        "\n",
        "    return group_reports\n",
        "\n",
        "\n"
      ]
    },
    {
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      "execution_count": null,
      "id": "a7f30889-7bc6-4d15-b53c-93c9bc8cbe5b",
      "metadata": {
        "id": "a7f30889-7bc6-4d15-b53c-93c9bc8cbe5b"
      },
      "outputs": [],
      "source": [
        "# Evaluate across races for each model\n",
        "group_reports_sao2 = evaluate_across_races(X_test, y_test, y_pred_sao2, y_pred_proba_sao2, final_standardized_test, model_sao2, features_sao2)\n",
        "\n",
        "group_reports_spo2 = evaluate_across_races(X_test, y_test, y_pred_spo2, y_pred_proba_spo2, final_standardized_test, model_spo2, features_spo2)\n",
        "\n",
        "group_reports_exaggerated_spo2 = evaluate_across_races(X_test, y_test, y_pred_exaggerated_spo2, y_pred_proba_exaggerated_spo2, final_standardized_test, model_exaggerated_spo2, features_exaggerated_spo2)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "group_reports_sao2['features'] = 'SaO2'\n",
        "group_reports_spo2['features'] = 'SpO2'\n",
        "group_reports_exaggerated_spo2['features'] = 'Exaggerated SpO2'\n",
        "\n",
        "all_reports = pd.concat([group_reports_sao2,group_reports_spo2,group_reports_exaggerated_spo2],axis=0)\n",
        "\n",
        "all_reports.sort_values(['race_ethnicity','features'])[['features','race_ethnicity', 'auc', 'Weighted Precision', 'Weighted Recall',\n",
        "       'Weighted F1-Score', 'balanced_accuracy']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "hr30pn1U1nNL",
        "outputId": "f34e5569-3dea-41fb-a8c1-60a78f336da7"
      },
      "id": "hr30pn1U1nNL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           features      race_ethnicity       auc  Weighted Precision  \\\n",
              "3  Exaggerated SpO2               Asian  0.727519            0.852817   \n",
              "3              SaO2               Asian  0.726617            0.840190   \n",
              "3              SpO2               Asian  0.728421            0.852817   \n",
              "1  Exaggerated SpO2               Black  0.758489            0.797087   \n",
              "1              SaO2               Black  0.759869            0.797797   \n",
              "1              SpO2               Black  0.760139            0.805853   \n",
              "2  Exaggerated SpO2  Hispanic OR Latino  0.782582            0.801163   \n",
              "2              SaO2  Hispanic OR Latino  0.775523            0.792869   \n",
              "2              SpO2  Hispanic OR Latino  0.782923            0.801163   \n",
              "0  Exaggerated SpO2               White  0.789874            0.825862   \n",
              "0              SaO2               White  0.786374            0.825589   \n",
              "0              SpO2               White  0.789897            0.826829   \n",
              "\n",
              "   Weighted Recall  Weighted F1-Score  balanced_accuracy  \n",
              "3         0.860759           0.819215           0.576241  \n",
              "3         0.854430           0.807050           0.556241  \n",
              "3         0.860759           0.819215           0.576241  \n",
              "1         0.832432           0.794723           0.572467  \n",
              "1         0.832432           0.796895           0.577410  \n",
              "1         0.836757           0.801089           0.582496  \n",
              "2         0.822014           0.783712           0.597269  \n",
              "2         0.817330           0.775786           0.585641  \n",
              "2         0.822014           0.783712           0.597269  \n",
              "0         0.844906           0.808421           0.594704  \n",
              "0         0.844640           0.807706           0.593354  \n",
              "0         0.845172           0.808235           0.593974  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>features</th>\n",
              "      <th>race_ethnicity</th>\n",
              "      <th>auc</th>\n",
              "      <th>Weighted Precision</th>\n",
              "      <th>Weighted Recall</th>\n",
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              "      <th>3</th>\n",
              "      <td>Exaggerated SpO2</td>\n",
              "      <td>Asian</td>\n",
              "      <td>0.727519</td>\n",
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              "      <td>0.819215</td>\n",
              "      <td>0.576241</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>SaO2</td>\n",
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              "      <th>3</th>\n",
              "      <td>SpO2</td>\n",
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              "      <td>0.759869</td>\n",
              "      <td>0.797797</td>\n",
              "      <td>0.832432</td>\n",
              "      <td>0.796895</td>\n",
              "      <td>0.577410</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SpO2</td>\n",
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              "      <td>0.760139</td>\n",
              "      <td>0.805853</td>\n",
              "      <td>0.836757</td>\n",
              "      <td>0.801089</td>\n",
              "      <td>0.582496</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>Exaggerated SpO2</td>\n",
              "      <td>Hispanic OR Latino</td>\n",
              "      <td>0.782582</td>\n",
              "      <td>0.801163</td>\n",
              "      <td>0.822014</td>\n",
              "      <td>0.783712</td>\n",
              "      <td>0.597269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SaO2</td>\n",
              "      <td>Hispanic OR Latino</td>\n",
              "      <td>0.775523</td>\n",
              "      <td>0.792869</td>\n",
              "      <td>0.817330</td>\n",
              "      <td>0.775786</td>\n",
              "      <td>0.585641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SpO2</td>\n",
              "      <td>Hispanic OR Latino</td>\n",
              "      <td>0.782923</td>\n",
              "      <td>0.801163</td>\n",
              "      <td>0.822014</td>\n",
              "      <td>0.783712</td>\n",
              "      <td>0.597269</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0.844906</td>\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2675aa04-efff-48b1-839a-b0d704f47ab5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"        # it is ok to leave it - given it demonstrates the exagerated data does not influence the other races\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"features\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Exaggerated SpO2\",\n          \"SaO2\",\n          \"SpO2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race_ethnicity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Black\",\n          \"White\",\n          \"Asian\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.024741273733248643,\n        \"min\": 0.7266165413533835,\n        \"max\": 0.7898966384150288,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.7863739379708479,\n          0.7898735783435427,\n          0.7275187969924812\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weighted Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021959607714638188,\n        \"min\": 0.7928687558710861,\n        \"max\": 0.8528170761975676,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.8255886647437298,\n          0.8401898734177216,\n          0.8011627429228809\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weighted Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014847964246393047,\n        \"min\": 0.8173302107728337,\n        \"max\": 0.8607594936708861,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.8446395317903698,\n          0.8544303797468354,\n          0.8173302107728337\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weighted F1-Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013944716053671397,\n        \"min\": 0.7757863739371406,\n        \"max\": 0.8192145407335281,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.8077055573109918,\n          0.8070498557451746,\n          0.78371204928582\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balanced_accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01251978625829808,\n        \"min\": 0.5562406015037593,\n        \"max\": 0.5972686353406533,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.5933535749516379,\n          0.5562406015037593,\n          0.5972686353406533\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Question: What key insights can you derive from this table?\n",
        "- Your answer: ...."
      ],
      "metadata": {
        "id": "cN03jg0wK0ty"
      },
      "id": "cN03jg0wK0ty"
    },
    {
      "cell_type": "markdown",
      "id": "b2a23e37-5569-4849-8094-ba6957e3348f",
      "metadata": {
        "id": "b2a23e37-5569-4849-8094-ba6957e3348f"
      },
      "source": [
        "Reminder:\n",
        "- Recall: This is critical in your context because it measures the ability of the model to identify all actual positive cases (e.g., patients who died). High recall ensures that the model doesn't miss many critical cases, which is essential in a healthcare setting.\n",
        "\n",
        "- Precision: While not as critical as recall, precision indicates the proportion of positive identifications that are actually correct. It helps in understanding the false positive rate.\n",
        "\n",
        "- F1-Score: This metric provides a balance between precision and recall. It's useful when you want a single metric that considers both false positives and false negatives.\n",
        "\n",
        "- Accuracy: While it gives an overall performance measure, it can be misleading in imbalanced datasets (e.g., more patients survive than die).\n",
        "\n",
        "- AUC (Area Under the Curve): This metric helps understand the model's ability to distinguish between positive and negative cases across various threshold settings. It’s useful for evaluating overall model performance.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calibration Plot"
      ],
      "metadata": {
        "id": "01tQ-tg-3zdf"
      },
      "id": "01tQ-tg-3zdf"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d6257714-93fe-4ce2-8097-1182f36b5c4b",
      "metadata": {
        "id": "d6257714-93fe-4ce2-8097-1182f36b5c4b",
        "outputId": "5857243b-21ca-476c-f3e5-579e402e6fb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.calibration import calibration_curve\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Calibration curve for model_sao2\n",
        "prob_true_sao2, prob_pred_sao2 = calibration_curve(y_test, y_pred_proba_sao2, n_bins=10)\n",
        "\n",
        "# Calibration curve for model_spo2\n",
        "prob_true_spo2, prob_pred_spo2 = calibration_curve(y_test, y_pred_proba_spo2, n_bins=10)\n",
        "\n",
        "# Calibration curve for model_exaggerated_spo2\n",
        "prob_true_exaggerated_spo2, prob_pred_exaggerated_spo2 = calibration_curve(y_test, y_pred_proba_exaggerated_spo2, n_bins=10)\n",
        "\n",
        "# Plot calibration curves\n",
        "plt.figure(figsize=(10, 7))\n",
        "\n",
        "# Plot calibration curve for model_sao2\n",
        "plt.plot(prob_pred_sao2, prob_true_sao2, marker='o', label='Model SAO2', color='blue', linewidth=0.5)\n",
        "\n",
        "# Plot calibration curve for model_spo2\n",
        "plt.plot(prob_pred_spo2, prob_true_spo2, marker='o', label='Model SPO2', color='orange', linewidth=0.5)\n",
        "\n",
        "# Plot calibration curve for model_exaggerated_spo2\n",
        "plt.plot(prob_pred_exaggerated_spo2, prob_true_exaggerated_spo2, marker='o', label='Model Exaggerated SPO2', color='green', linewidth=0.5)\n",
        "\n",
        "# Plot perfectly calibrated line\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly Calibrated', linewidth=1)\n",
        "\n",
        "\n",
        "plt.xlabel('Predicted Probability')\n",
        "plt.ylabel('True Probability')\n",
        "plt.title('Calibration Curves')\n",
        "plt.legend()\n",
        "plt.show()\n"
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        "- Question: What key insights can you derive from this figure?\n",
        "- Your answer: ....\n"
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        " * Try the same pipeline to assess different outcomes:\n",
        "   - Ordinal outcomes, such as CVSOFA (Cardiovascular Sequential Organ Failure Assessment) and RSOFA (Respiratory Sequential Organ Failure Assessment) scores.\n",
        "   - Continuous outcomes, such as creatinine and lactate levels.\n",
        "  \n",
        " * What observations do you see?"
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