mimic-weekend-effect.ipynb

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    "colab": {
      "name": "challenge-demo.ipynb",
      "provenance": []
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yi5bCFZBMWkj",
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      "source": [
        "# Python demo for the 2018 BHI & BSN Data Challenge\n",
        "\n",
        "This notebook provides a simple introduction to analysing the MIMIC-III database. It was created as a demonstrator for the [2018 BHI & BSN Data Challenge](https://mimic.physionet.org/events/bhibsn-challenge/), which explores the following question:\n",
        "\n",
        "> Are patients admitted to the intensive care unit (ICU) on a weekend more likely to die in the hospital than those admitted on a weekday?\n",
        "\n",
        "We have provided an example slide template for final presentations (`slide-template.pptx`) at: https://github.com/MIT-LCP/bhi-bsn-challenge. There is no obligation to use it!\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h9Vof4xQMWkl",
        "colab_type": "text"
      },
      "source": [
        "## Background on MIMIC-III\n",
        "\n",
        "MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. \n",
        "\n",
        "Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. \n",
        "\n",
        "For details, see: https://mimic.physionet.org/. The data is downloaded as 26 CSV files, which can then be loaded into a database system. Scripts for loading the data into Postgres are provided in the [MIMIC Code Repository](https://mimic.physionet.org/gettingstarted/dbsetup/). A demo dataset is also available: https://mimic.physionet.org/gettingstarted/demo/\n",
        "\n",
        "Points to note:\n",
        "\n",
        "- A patient-level shift has been applied to dates. Day of week is retained. \n",
        "- Patients aged >89 years on first admission have been reassigned an age of ~300 years.\n",
        "- Patients may have multiple hospital admissions. Each hospital admission may comprise multiple ICU stays (e.g. a patient may visit the ICU, leave for surgery, and then return to the ICU for recovery, all within a single hospital admission).\n",
        "\n",
        "If you need help getting set up with access to MIMIC-III, please contact `data-challenge@physionet.org`.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLtC2c3QMWkm",
        "colab_type": "text"
      },
      "source": [
        "# Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcJaxoqLMWkn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data processing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import itertools\n",
        "\n",
        "# Stats libraries\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "import scipy.stats\n",
        "\n",
        "# Image libraries\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Make pandas dataframes prettier\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "# Access data using Google BigQuery.\n",
        "import os\n",
        "from google.colab import auth\n",
        "from google.cloud import bigquery"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GtyznQqKNe4j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "01875e72-77d2-4106-a04c-a439b53cc5dd"
      },
      "source": [
        "# install and import tableone\n",
        "!pip install tableone\n",
        "from tableone import TableOne"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tableone in /usr/local/lib/python3.6/dist-packages (0.6.6)\n",
            "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from tableone) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.12.1 in /usr/local/lib/python3.6/dist-packages (from tableone) (1.17.5)\n",
            "Requirement already satisfied: statsmodels>=0.8.0 in /usr/local/lib/python3.6/dist-packages (from tableone) (0.10.2)\n",
            "Requirement already satisfied: pandas>=0.22.0 in /usr/local/lib/python3.6/dist-packages (from tableone) (0.25.3)\n",
            "Requirement already satisfied: tabulate>=0.8.2 in /usr/local/lib/python3.6/dist-packages (from tableone) (0.8.6)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from statsmodels>=0.8.0->tableone) (0.5.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.22.0->tableone) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.22.0->tableone) (2.6.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.4.0->statsmodels>=0.8.0->tableone) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxrVxcLdMWks",
        "colab_type": "text"
      },
      "source": [
        "# Connect to the MIMIC-III database\n",
        "\n",
        "Before running any queries, you need to first authenticate yourself by running the following cell. If you are running it for the first time, it will ask you to follow a link to log in using your Gmail account, and accept the data access requests to your profile. Once this is done, it will generate a string of verification code, which you should paste back to the cell below and press enter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4B6TPqBvMWks",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "auth.authenticate_user()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ps3vy94mMWkx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# add your project ID for billing\n",
        "project_id='hack-aotearoa'\n",
        "os.environ[\"GOOGLE_CLOUD_PROJECT\"]=project_id"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbS3XgD9MWkz",
        "colab_type": "text"
      },
      "source": [
        "# Extract data from MIMIC-III and assign to a Pandas DataFrame\n",
        "\n",
        "The following query extracts a simple dataset from the MIMIC-III database, comprising demographics, hospital and ICU admission times, and a severity of illness score ([OASIS](https://www.ncbi.nlm.nih.gov/pubmed/23660729)).\n",
        "\n",
        "Before running this query, you must first build the `icustay_detail` and `oasis` materialized views. Code for building these views is available in the MIMIC Code Repository:\n",
        "- `icustay_detail`: https://github.com/MIT-LCP/mimic-code/tree/master/concepts/demographics\n",
        "- `oasis`: https://github.com/MIT-LCP/mimic-code/tree/master/concepts/severityscores\n",
        "\n",
        "You will notice that our example restricts the analysis to:\n",
        "\n",
        "- first hospital admissions \n",
        "- patients who were `>= 16` years at time of hospital admission.\n",
        "- the first ICU stay (patients may move to the ICU multiple times within a hospital stay)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zIFQQ8EMWkz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Run query and assign the results to a Pandas DataFrame\n",
        "# Requires the icustay_detail view from:\n",
        "# https://github.com/MIT-LCP/mimic-code/tree/master/concepts/demographics\n",
        "# And the OASIS score from:\n",
        "# https://github.com/MIT-LCP/mimic-code/tree/master/concepts/severityscores\n",
        "%%bigquery data\n",
        "\n",
        "WITH first_icu AS (\n",
        "    SELECT i.subject_id, i.hadm_id, i.icustay_id, i.gender, i.admittime admittime_hospital, \n",
        "      i.dischtime dischtime_hospital, i.los_hospital, i.admission_age, i.admission_type, \n",
        "      i.hospital_expire_flag, i.intime intime_icu, i.outtime outtime_icu, i.los_icu, \n",
        "      s.first_careunit\n",
        "    FROM `physionet-data.mimiciii_derived.icustay_detail` i\n",
        "    LEFT JOIN `physionet-data.mimiciii_clinical.icustays` s\n",
        "    ON i.icustay_id = s.icustay_id\n",
        "    WHERE i.hospstay_seq = 1\n",
        "      AND i.icustay_seq = 1\n",
        "      AND i.admission_age >= 16\n",
        ")\n",
        "SELECT f.*, o.icustay_expire_flag, o.oasis, o.oasis_prob\n",
        "FROM first_icu f\n",
        "LEFT JOIN `physionet-data.mimiciii_derived.oasis` o\n",
        "ON f.icustay_id = o.icustay_id;"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bu442pAsMWk1",
        "colab_type": "text"
      },
      "source": [
        "# Check the extracted data\n",
        "\n",
        "It is always a good idea to inspect the data after you have extracted it.  We will look at the first six patients (rows), and then check the number of rows, and get some summary statistics of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12Aqg89AMWk2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "7cc73d71-6d77-4823-e826-c573dbad351c"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['subject_id', 'hadm_id', 'icustay_id', 'gender', 'admittime_hospital',\n",
              "       'dischtime_hospital', 'los_hospital', 'admission_age', 'admission_type',\n",
              "       'hospital_expire_flag', 'intime_icu', 'outtime_icu', 'los_icu',\n",
              "       'first_careunit', 'icustay_expire_flag', 'oasis', 'oasis_prob'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
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    {
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        "id": "20zN-HEUMWk4",
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          "height": 309
        },
        "outputId": "e469cb69-cbc3-46af-b93c-fa898c22f97e"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>subject_id</th>\n",
              "      <th>hadm_id</th>\n",
              "      <th>icustay_id</th>\n",
              "      <th>gender</th>\n",
              "      <th>admittime_hospital</th>\n",
              "      <th>dischtime_hospital</th>\n",
              "      <th>los_hospital</th>\n",
              "      <th>admission_age</th>\n",
              "      <th>admission_type</th>\n",
              "      <th>hospital_expire_flag</th>\n",
              "      <th>intime_icu</th>\n",
              "      <th>outtime_icu</th>\n",
              "      <th>los_icu</th>\n",
              "      <th>first_careunit</th>\n",
              "      <th>icustay_expire_flag</th>\n",
              "      <th>oasis</th>\n",
              "      <th>oasis_prob</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>145834</td>\n",
              "      <td>211552</td>\n",
              "      <td>M</td>\n",
              "      <td>2101-10-20 19:08:00</td>\n",
              "      <td>2101-10-31 13:58:00</td>\n",
              "      <td>11</td>\n",
              "      <td>76</td>\n",
              "      <td>EMERGENCY</td>\n",
              "      <td>0</td>\n",
              "      <td>2101-10-20 19:10:11</td>\n",
              "      <td>2101-10-26 20:43:09</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MICU</td>\n",
              "      <td>0</td>\n",
              "      <td>57</td>\n",
              "      <td>0.748927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>107064</td>\n",
              "      <td>228232</td>\n",
              "      <td>F</td>\n",
              "      <td>2175-05-30 07:15:00</td>\n",
              "      <td>2175-06-15 16:00:00</td>\n",
              "      <td>16</td>\n",
              "      <td>66</td>\n",
              "      <td>ELECTIVE</td>\n",
              "      <td>0</td>\n",
              "      <td>2175-05-30 21:30:54</td>\n",
              "      <td>2175-06-03 13:39:54</td>\n",
              "      <td>4.0</td>\n",
              "      <td>SICU</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>0.008392</td>\n",
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              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>185910</td>\n",
              "      <td>248910</td>\n",
              "      <td>M</td>\n",
              "      <td>2166-08-10 00:28:00</td>\n",
              "      <td>2166-09-04 11:30:00</td>\n",
              "      <td>25</td>\n",
              "      <td>76</td>\n",
              "      <td>EMERGENCY</td>\n",
              "      <td>0</td>\n",
              "      <td>2166-08-10 00:29:36</td>\n",
              "      <td>2166-09-04 13:39:23</td>\n",
              "      <td>25.0</td>\n",
              "      <td>CCU</td>\n",
              "      <td>0</td>\n",
              "      <td>54</td>\n",
              "      <td>0.670490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>181711</td>\n",
              "      <td>275642</td>\n",
              "      <td>F</td>\n",
              "      <td>2104-01-02 02:01:00</td>\n",
              "      <td>2104-01-08 10:30:00</td>\n",
              "      <td>6</td>\n",
              "      <td>300</td>\n",
              "      <td>EMERGENCY</td>\n",
              "      <td>1</td>\n",
              "      <td>2104-01-02 02:02:39</td>\n",
              "      <td>2104-01-03 22:25:29</td>\n",
              "      <td>1.0</td>\n",
              "      <td>SICU</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>0.611922</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>98</td>\n",
              "      <td>188606</td>\n",
              "      <td>216929</td>\n",
              "      <td>F</td>\n",
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              "      <td>2137-04-09 10:38:00</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
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              "      <td>2137-04-02 15:15:39</td>\n",
              "      <td>2137-04-04 21:06:16</td>\n",
              "      <td>2.0</td>\n",
              "      <td>SICU</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>0.008392</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   subject_id  hadm_id  icustay_id  ... icustay_expire_flag oasis oasis_prob\n",
              "0           3   145834      211552  ...                   0    57   0.748927\n",
              "1           6   107064      228232  ...                   0    11   0.008392\n",
              "2          38   185910      248910  ...                   0    54   0.670490\n",
              "3          56   181711      275642  ...                   0    52   0.611922\n",
              "4          98   188606      216929  ...                   0    11   0.008392\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
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        },
        "outputId": "c8ab7ef1-0630-41bd-8ade-b4f79edf8c34"
      },
      "source": [
        "data.describe().T"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>subject_id</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>38266.820037</td>\n",
              "      <td>29376.431490</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>13980.250000</td>\n",
              "      <td>27912.500000</td>\n",
              "      <td>62963.250000</td>\n",
              "      <td>99999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hadm_id</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>150037.148919</td>\n",
              "      <td>28944.929045</td>\n",
              "      <td>100001.000000</td>\n",
              "      <td>124928.500000</td>\n",
              "      <td>150092.500000</td>\n",
              "      <td>175222.250000</td>\n",
              "      <td>199999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>icustay_id</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>250222.157218</td>\n",
              "      <td>28913.703899</td>\n",
              "      <td>200003.000000</td>\n",
              "      <td>225240.250000</td>\n",
              "      <td>250278.000000</td>\n",
              "      <td>275287.000000</td>\n",
              "      <td>299999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>los_hospital</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>9.853831</td>\n",
              "      <td>10.705233</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>295.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>admission_age</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>74.562970</td>\n",
              "      <td>55.115176</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>310.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hospital_expire_flag</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>0.114736</td>\n",
              "      <td>0.318708</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>los_icu</th>\n",
              "      <td>38556.0</td>\n",
              "      <td>3.987395</td>\n",
              "      <td>6.018559</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>154.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>icustay_expire_flag</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>0.082836</td>\n",
              "      <td>0.275638</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oasis</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>30.904015</td>\n",
              "      <td>8.810747</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oasis_prob</th>\n",
              "      <td>38558.0</td>\n",
              "      <td>0.138850</td>\n",
              "      <td>0.136895</td>\n",
              "      <td>0.003042</td>\n",
              "      <td>0.048012</td>\n",
              "      <td>0.087098</td>\n",
              "      <td>0.188911</td>\n",
              "      <td>0.939936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        count           mean  ...            75%            max\n",
              "subject_id            38558.0   38266.820037  ...   62963.250000   99999.000000\n",
              "hadm_id               38558.0  150037.148919  ...  175222.250000  199999.000000\n",
              "icustay_id            38558.0  250222.157218  ...  275287.000000  299999.000000\n",
              "los_hospital          38558.0       9.853831  ...      12.000000     295.000000\n",
              "admission_age         38558.0      74.562970  ...      78.000000     310.000000\n",
              "hospital_expire_flag  38558.0       0.114736  ...       0.000000       1.000000\n",
              "los_icu               38556.0       3.987395  ...       4.000000     154.000000\n",
              "icustay_expire_flag   38558.0       0.082836  ...       0.000000       1.000000\n",
              "oasis                 38558.0      30.904015  ...      37.000000      70.000000\n",
              "oasis_prob            38558.0       0.138850  ...       0.188911       0.939936\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwS3Gt-lMWk_",
        "colab_type": "text"
      },
      "source": [
        "# Add day of week to DataFrame\n",
        "\n",
        "If we are going to examine the weekend effect, we need to pull this out of the dataset, as you can see, all we have above are dates. We will define a weekend, as anytime between Saturday (00:00:00) until Sunday (23:59:59). The dates above are shifted, and that's why they look odd, but they are matched on the day of week, so this aspect is preserved."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2F1Pmf5EMWlA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "4dbe6b48-527a-446a-8e4e-caa25b6694f0"
      },
      "source": [
        "data['admitday_hospital'] = data.admittime_hospital.dt.weekday_name\n",
        "data['dischday_hospital'] = data.dischtime_hospital.dt.weekday_name\n",
        "data['inday_icu'] = data.intime_icu.dt.weekday_name\n",
        "data['inday_icu_seq'] = data.intime_icu.dt.weekday\n",
        "data['outday_icu'] = data.outtime_icu.dt.weekday_name\n",
        "data.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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 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>subject_id</th>\n",
              "      <th>hadm_id</th>\n",
              "      <th>icustay_id</th>\n",
              "      <th>gender</th>\n",
              "      <th>admittime_hospital</th>\n",
              "      <th>dischtime_hospital</th>\n",
              "      <th>los_hospital</th>\n",
              "      <th>admission_age</th>\n",
              "      <th>admission_type</th>\n",
              "      <th>hospital_expire_flag</th>\n",
              "      <th>intime_icu</th>\n",
              "      <th>outtime_icu</th>\n",
              "      <th>los_icu</th>\n",
              "      <th>first_careunit</th>\n",
              "      <th>icustay_expire_flag</th>\n",
              "      <th>oasis</th>\n",
              "      <th>oasis_prob</th>\n",
              "      <th>admitday_hospital</th>\n",
              "      <th>dischday_hospital</th>\n",
              "      <th>inday_icu</th>\n",
              "      <th>inday_icu_seq</th>\n",
              "      <th>outday_icu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>145834</td>\n",
              "      <td>211552</td>\n",
              "      <td>M</td>\n",
              "      <td>2101-10-20 19:08:00</td>\n",
              "      <td>2101-10-31 13:58:00</td>\n",
              "      <td>11</td>\n",
              "      <td>76</td>\n",
              "      <td>EMERGENCY</td>\n",
              "      <td>0</td>\n",
              "      <td>2101-10-20 19:10:11</td>\n",
              "      <td>2101-10-26 20:43:09</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MICU</td>\n",
              "      <td>0</td>\n",
              "      <td>57</td>\n",
              "      <td>0.748927</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>Monday</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>3</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>107064</td>\n",
              "      <td>228232</td>\n",
              "      <td>F</td>\n",
              "      <td>2175-05-30 07:15:00</td>\n",
              "      <td>2175-06-15 16:00:00</td>\n",
              "      <td>16</td>\n",
              "      <td>66</td>\n",
              "      <td>ELECTIVE</td>\n",
              "      <td>0</td>\n",
              "      <td>2175-05-30 21:30:54</td>\n",
              "      <td>2175-06-03 13:39:54</td>\n",
              "      <td>4.0</td>\n",
              "      <td>SICU</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>0.008392</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>1</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>185910</td>\n",
              "      <td>248910</td>\n",
              "      <td>M</td>\n",
              "      <td>2166-08-10 00:28:00</td>\n",
              "      <td>2166-09-04 11:30:00</td>\n",
              "      <td>25</td>\n",
              "      <td>76</td>\n",
              "      <td>EMERGENCY</td>\n",
              "      <td>0</td>\n",
              "      <td>2166-08-10 00:29:36</td>\n",
              "      <td>2166-09-04 13:39:23</td>\n",
              "      <td>25.0</td>\n",
              "      <td>CCU</td>\n",
              "      <td>0</td>\n",
              "      <td>54</td>\n",
              "      <td>0.670490</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>6</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>181711</td>\n",
              "      <td>275642</td>\n",
              "      <td>F</td>\n",
              "      <td>2104-01-02 02:01:00</td>\n",
              "      <td>2104-01-08 10:30:00</td>\n",
              "      <td>6</td>\n",
              "      <td>300</td>\n",
              "      <td>EMERGENCY</td>\n",
              "      <td>1</td>\n",
              "      <td>2104-01-02 02:02:39</td>\n",
              "      <td>2104-01-03 22:25:29</td>\n",
              "      <td>1.0</td>\n",
              "      <td>SICU</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>0.611922</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>2</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>98</td>\n",
              "      <td>188606</td>\n",
              "      <td>216929</td>\n",
              "      <td>F</td>\n",
              "      <td>2137-04-02 08:00:00</td>\n",
              "      <td>2137-04-09 10:38:00</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>ELECTIVE</td>\n",
              "      <td>0</td>\n",
              "      <td>2137-04-02 15:15:39</td>\n",
              "      <td>2137-04-04 21:06:16</td>\n",
              "      <td>2.0</td>\n",
              "      <td>SICU</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>0.008392</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   subject_id  hadm_id  icustay_id  ...  inday_icu inday_icu_seq outday_icu\n",
              "0           3   145834      211552  ...   Thursday             3  Wednesday\n",
              "1           6   107064      228232  ...    Tuesday             1   Saturday\n",
              "2          38   185910      248910  ...     Sunday             6   Thursday\n",
              "3          56   181711      275642  ...  Wednesday             2   Thursday\n",
              "4          98   188606      216929  ...    Tuesday             1   Thursday\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qK4ieh6MWlC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "dd2fb58e-43f2-41dc-bf2e-b084ecc7e563"
      },
      "source": [
        "data['inday_icu'].value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Friday       6263\n",
              "Tuesday      6141\n",
              "Monday       6097\n",
              "Wednesday    5985\n",
              "Thursday     5877\n",
              "Saturday     4235\n",
              "Sunday       3960\n",
              "Name: inday_icu, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jx2E-tWzMWlE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "254879c9-357a-4633-c1e1-d9fcf9d91412"
      },
      "source": [
        "# create weekday vs weekend column for icu_intime \n",
        "data['inday_icu_wkd'] = np.where(data.intime_icu.dt.weekday <= 4, \n",
        "                                 'weekday','weekend')\n",
        "data['inday_icu_wkd'].value_counts()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weekday    30363\n",
              "weekend     8195\n",
              "Name: inday_icu_wkd, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "bnvClxjQMWlG",
        "colab_type": "text"
      },
      "source": [
        "# Produce some Summary Statistics by DOW and Weekday vs. Weekend\n",
        "\n",
        "Next, it's good to look at some basic summaries of the data.  We will compute simple averages and percentages/counts for each of the variables we have extracted, and look at it by day of week and weekend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIik2OotMWlH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "01da588b-015c-4510-e3af-31f285bdc901"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['subject_id', 'hadm_id', 'icustay_id', 'gender', 'admittime_hospital',\n",
              "       'dischtime_hospital', 'los_hospital', 'admission_age', 'admission_type',\n",
              "       'hospital_expire_flag', 'intime_icu', 'outtime_icu', 'los_icu',\n",
              "       'first_careunit', 'icustay_expire_flag', 'oasis', 'oasis_prob',\n",
              "       'admitday_hospital', 'dischday_hospital', 'inday_icu', 'inday_icu_seq',\n",
              "       'outday_icu', 'inday_icu_wkd'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALKoIYjaMWlJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 762
        },
        "outputId": "4662f8ca-9688-41e5-cccf-4f3af7327557"
      },
      "source": [
        "columns = ['gender', 'los_hospital', 'admission_age', 'admission_type', 'hospital_expire_flag', \n",
        "           'los_icu','icustay_expire_flag', 'oasis', 'oasis_prob', 'first_careunit',\n",
        "           'inday_icu_wkd']\n",
        "\n",
        "groupby = 'inday_icu'\n",
        "\n",
        "pval = False\n",
        "\n",
        "categorical = ['gender','admission_type','hospital_expire_flag','icustay_expire_flag',\n",
        "               'first_careunit','inday_icu_wkd']\n",
        "\n",
        "t = TableOne(data, columns=columns, categorical=categorical, groupby=groupby, pval=pval)\n",
        "t.tableone"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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=\"8\" halign=\"left\">Grouped by inday_icu</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Missing</th>\n",
              "      <th>Friday</th>\n",
              "      <th>Monday</th>\n",
              "      <th>Saturday</th>\n",
              "      <th>Sunday</th>\n",
              "      <th>Thursday</th>\n",
              "      <th>Tuesday</th>\n",
              "      <th>Wednesday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n</th>\n",
              "      <th></th>\n",
              "      <td></td>\n",
              "      <td>6263</td>\n",
              "      <td>6097</td>\n",
              "      <td>4235</td>\n",
              "      <td>3960</td>\n",
              "      <td>5877</td>\n",
              "      <td>6141</td>\n",
              "      <td>5985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">gender</th>\n",
              "      <th>F</th>\n",
              "      <td>0</td>\n",
              "      <td>2662 (42.5)</td>\n",
              "      <td>2559 (42.0)</td>\n",
              "      <td>1857 (43.8)</td>\n",
              "      <td>1736 (43.8)</td>\n",
              "      <td>2603 (44.3)</td>\n",
              "      <td>2671 (43.5)</td>\n",
              "      <td>2636 (44.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td></td>\n",
              "      <td>3601 (57.5)</td>\n",
              "      <td>3538 (58.0)</td>\n",
              "      <td>2378 (56.2)</td>\n",
              "      <td>2224 (56.2)</td>\n",
              "      <td>3274 (55.7)</td>\n",
              "      <td>3470 (56.5)</td>\n",
              "      <td>3349 (56.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>los_hospital</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>10.2 (11.6)</td>\n",
              "      <td>9.7 (9.9)</td>\n",
              "      <td>10.0 (10.9)</td>\n",
              "      <td>9.8 (10.7)</td>\n",
              "      <td>9.8 (10.9)</td>\n",
              "      <td>9.8 (10.6)</td>\n",
              "      <td>9.7 (10.2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>admission_age</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>74.6 (53.2)</td>\n",
              "      <td>73.1 (51.4)</td>\n",
              "      <td>73.9 (58.6)</td>\n",
              "      <td>75.3 (60.7)</td>\n",
              "      <td>75.5 (55.5)</td>\n",
              "      <td>75.5 (55.2)</td>\n",
              "      <td>74.2 (53.9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">admission_type</th>\n",
              "      <th>ELECTIVE</th>\n",
              "      <td>0</td>\n",
              "      <td>1016 (16.2)</td>\n",
              "      <td>1265 (20.7)</td>\n",
              "      <td>162 (3.8)</td>\n",
              "      <td>101 (2.6)</td>\n",
              "      <td>999 (17.0)</td>\n",
              "      <td>1292 (21.0)</td>\n",
              "      <td>1243 (20.8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EMERGENCY</th>\n",
              "      <td></td>\n",
              "      <td>5118 (81.7)</td>\n",
              "      <td>4687 (76.9)</td>\n",
              "      <td>3852 (91.0)</td>\n",
              "      <td>3681 (93.0)</td>\n",
              "      <td>4747 (80.8)</td>\n",
              "      <td>4704 (76.6)</td>\n",
              "      <td>4600 (76.9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URGENT</th>\n",
              "      <td></td>\n",
              "      <td>129 (2.1)</td>\n",
              "      <td>145 (2.4)</td>\n",
              "      <td>221 (5.2)</td>\n",
              "      <td>178 (4.5)</td>\n",
              "      <td>131 (2.2)</td>\n",
              "      <td>145 (2.4)</td>\n",
              "      <td>142 (2.4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">hospital_expire_flag</th>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>5576 (89.0)</td>\n",
              "      <td>5468 (89.7)</td>\n",
              "      <td>3658 (86.4)</td>\n",
              "      <td>3388 (85.6)</td>\n",
              "      <td>5203 (88.5)</td>\n",
              "      <td>5491 (89.4)</td>\n",
              "      <td>5350 (89.4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>687 (11.0)</td>\n",
              "      <td>629 (10.3)</td>\n",
              "      <td>577 (13.6)</td>\n",
              "      <td>572 (14.4)</td>\n",
              "      <td>674 (11.5)</td>\n",
              "      <td>650 (10.6)</td>\n",
              "      <td>635 (10.6)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>los_icu</th>\n",
              "      <th></th>\n",
              "      <td>2</td>\n",
              "      <td>4.1 (6.2)</td>\n",
              "      <td>3.8 (5.5)</td>\n",
              "      <td>4.3 (6.6)</td>\n",
              "      <td>4.3 (6.3)</td>\n",
              "      <td>3.9 (6.1)</td>\n",
              "      <td>3.7 (5.7)</td>\n",
              "      <td>4.0 (5.9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">icustay_expire_flag</th>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>5768 (92.1)</td>\n",
              "      <td>5650 (92.7)</td>\n",
              "      <td>3811 (90.0)</td>\n",
              "      <td>3548 (89.6)</td>\n",
              "      <td>5400 (91.9)</td>\n",
              "      <td>5673 (92.4)</td>\n",
              "      <td>5514 (92.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>495 (7.9)</td>\n",
              "      <td>447 (7.3)</td>\n",
              "      <td>424 (10.0)</td>\n",
              "      <td>412 (10.4)</td>\n",
              "      <td>477 (8.1)</td>\n",
              "      <td>468 (7.6)</td>\n",
              "      <td>471 (7.9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oasis</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>30.9 (8.9)</td>\n",
              "      <td>30.9 (8.6)</td>\n",
              "      <td>31.3 (9.0)</td>\n",
              "      <td>31.8 (8.8)</td>\n",
              "      <td>30.8 (8.7)</td>\n",
              "      <td>30.7 (8.6)</td>\n",
              "      <td>30.3 (9.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oasis_prob</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>0.1 (0.1)</td>\n",
              "      <td>0.1 (0.1)</td>\n",
              "      <td>0.1 (0.1)</td>\n",
              "      <td>0.2 (0.1)</td>\n",
              "      <td>0.1 (0.1)</td>\n",
              "      <td>0.1 (0.1)</td>\n",
              "      <td>0.1 (0.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">first_careunit</th>\n",
              "      <th>CCU</th>\n",
              "      <td>0</td>\n",
              "      <td>838 (13.4)</td>\n",
              "      <td>918 (15.1)</td>\n",
              "      <td>695 (16.4)</td>\n",
              "      <td>621 (15.7)</td>\n",
              "      <td>850 (14.5)</td>\n",
              "      <td>919 (15.0)</td>\n",
              "      <td>851 (14.2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CSRU</th>\n",
              "      <td></td>\n",
              "      <td>1416 (22.6)</td>\n",
              "      <td>1632 (26.8)</td>\n",
              "      <td>237 (5.6)</td>\n",
              "      <td>194 (4.9)</td>\n",
              "      <td>1282 (21.8)</td>\n",
              "      <td>1575 (25.6)</td>\n",
              "      <td>1268 (21.2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MICU</th>\n",
              "      <td></td>\n",
              "      <td>2139 (34.2)</td>\n",
              "      <td>1940 (31.8)</td>\n",
              "      <td>1765 (41.7)</td>\n",
              "      <td>1706 (43.1)</td>\n",
              "      <td>2020 (34.4)</td>\n",
              "      <td>2019 (32.9)</td>\n",
              "      <td>2020 (33.8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SICU</th>\n",
              "      <td></td>\n",
              "      <td>1044 (16.7)</td>\n",
              "      <td>865 (14.2)</td>\n",
              "      <td>743 (17.5)</td>\n",
              "      <td>743 (18.8)</td>\n",
              "      <td>996 (16.9)</td>\n",
              "      <td>933 (15.2)</td>\n",
              "      <td>1038 (17.3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TSICU</th>\n",
              "      <td></td>\n",
              "      <td>826 (13.2)</td>\n",
              "      <td>742 (12.2)</td>\n",
              "      <td>795 (18.8)</td>\n",
              "      <td>696 (17.6)</td>\n",
              "      <td>729 (12.4)</td>\n",
              "      <td>695 (11.3)</td>\n",
              "      <td>808 (13.5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">inday_icu_wkd</th>\n",
              "      <th>weekday</th>\n",
              "      <td>0</td>\n",
              "      <td>6263 (100.0)</td>\n",
              "      <td>6097 (100.0)</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>5877 (100.0)</td>\n",
              "      <td>6141 (100.0)</td>\n",
              "      <td>5985 (100.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weekend</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>4235 (100.0)</td>\n",
              "      <td>3960 (100.0)</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Grouped by inday_icu  ...              \n",
              "                                            Missing  ...     Wednesday\n",
              "n                                                    ...          5985\n",
              "gender               F                            0  ...   2636 (44.0)\n",
              "                     M                               ...   3349 (56.0)\n",
              "los_hospital                                      0  ...    9.7 (10.2)\n",
              "admission_age                                     0  ...   74.2 (53.9)\n",
              "admission_type       ELECTIVE                     0  ...   1243 (20.8)\n",
              "                     EMERGENCY                       ...   4600 (76.9)\n",
              "                     URGENT                          ...     142 (2.4)\n",
              "hospital_expire_flag 0                            0  ...   5350 (89.4)\n",
              "                     1                               ...    635 (10.6)\n",
              "los_icu                                           2  ...     4.0 (5.9)\n",
              "icustay_expire_flag  0                            0  ...   5514 (92.1)\n",
              "                     1                               ...     471 (7.9)\n",
              "oasis                                             0  ...    30.3 (9.0)\n",
              "oasis_prob                                        0  ...     0.1 (0.1)\n",
              "first_careunit       CCU                          0  ...    851 (14.2)\n",
              "                     CSRU                            ...   1268 (21.2)\n",
              "                     MICU                            ...   2020 (33.8)\n",
              "                     SICU                            ...   1038 (17.3)\n",
              "                     TSICU                           ...    808 (13.5)\n",
              "inday_icu_wkd        weekday                      0  ...  5985 (100.0)\n",
              "                     weekend                         ...              \n",
              "\n",
              "[22 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYoHIgdQMWlL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "outputId": "384afb73-07a4-4ab9-a66c-b95a027348b7"
      },
      "source": [
        "columns = ['gender', 'los_hospital', 'admission_age', 'admission_type', 'hospital_expire_flag', \n",
        "           'los_icu','icustay_expire_flag', 'oasis', 'oasis_prob', 'first_careunit']\n",
        "\n",
        "groupby = 'inday_icu_wkd'\n",
        "\n",
        "pval = False\n",
        "\n",
        "categorical = ['gender','admission_type','hospital_expire_flag','icustay_expire_flag',\n",
        "               'first_careunit']\n",
        "\n",
        "t = TableOne(data, columns=columns, categorical=categorical, groupby=groupby, pval=pval)\n",
        "t.tableone"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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=\"3\" halign=\"left\">Grouped by inday_icu_wkd</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Missing</th>\n",
              "      <th>weekday</th>\n",
              "      <th>weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>n</th>\n",
              "      <th></th>\n",
              "      <td></td>\n",
              "      <td>30363</td>\n",
              "      <td>8195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">gender</th>\n",
              "      <th>F</th>\n",
              "      <td>0</td>\n",
              "      <td>13131 (43.2)</td>\n",
              "      <td>3593 (43.8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td></td>\n",
              "      <td>17232 (56.8)</td>\n",
              "      <td>4602 (56.2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>los_hospital</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>9.9 (10.7)</td>\n",
              "      <td>9.9 (10.8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>admission_age</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>74.6 (53.8)</td>\n",
              "      <td>74.6 (59.6)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">admission_type</th>\n",
              "      <th>ELECTIVE</th>\n",
              "      <td>0</td>\n",
              "      <td>5815 (19.2)</td>\n",
              "      <td>263 (3.2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EMERGENCY</th>\n",
              "      <td></td>\n",
              "      <td>23856 (78.6)</td>\n",
              "      <td>7533 (91.9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URGENT</th>\n",
              "      <td></td>\n",
              "      <td>692 (2.3)</td>\n",
              "      <td>399 (4.9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">hospital_expire_flag</th>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>27088 (89.2)</td>\n",
              "      <td>7046 (86.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>3275 (10.8)</td>\n",
              "      <td>1149 (14.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>los_icu</th>\n",
              "      <th></th>\n",
              "      <td>2</td>\n",
              "      <td>3.9 (5.9)</td>\n",
              "      <td>4.3 (6.4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">icustay_expire_flag</th>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>28005 (92.2)</td>\n",
              "      <td>7359 (89.8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>2358 (7.8)</td>\n",
              "      <td>836 (10.2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oasis</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>30.7 (8.8)</td>\n",
              "      <td>31.5 (8.9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oasis_prob</th>\n",
              "      <th></th>\n",
              "      <td>0</td>\n",
              "      <td>0.1 (0.1)</td>\n",
              "      <td>0.1 (0.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">first_careunit</th>\n",
              "      <th>CCU</th>\n",
              "      <td>0</td>\n",
              "      <td>4376 (14.4)</td>\n",
              "      <td>1316 (16.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CSRU</th>\n",
              "      <td></td>\n",
              "      <td>7173 (23.6)</td>\n",
              "      <td>431 (5.3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MICU</th>\n",
              "      <td></td>\n",
              "      <td>10138 (33.4)</td>\n",
              "      <td>3471 (42.4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SICU</th>\n",
              "      <td></td>\n",
              "      <td>4876 (16.1)</td>\n",
              "      <td>1486 (18.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TSICU</th>\n",
              "      <td></td>\n",
              "      <td>3800 (12.5)</td>\n",
              "      <td>1491 (18.2)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Grouped by inday_icu_wkd  ...             \n",
              "                                                Missing  ...      weekend\n",
              "n                                                        ...         8195\n",
              "gender               F                                0  ...  3593 (43.8)\n",
              "                     M                                   ...  4602 (56.2)\n",
              "los_hospital                                          0  ...   9.9 (10.8)\n",
              "admission_age                                         0  ...  74.6 (59.6)\n",
              "admission_type       ELECTIVE                         0  ...    263 (3.2)\n",
              "                     EMERGENCY                           ...  7533 (91.9)\n",
              "                     URGENT                              ...    399 (4.9)\n",
              "hospital_expire_flag 0                                0  ...  7046 (86.0)\n",
              "                     1                                   ...  1149 (14.0)\n",
              "los_icu                                               2  ...    4.3 (6.4)\n",
              "icustay_expire_flag  0                                0  ...  7359 (89.8)\n",
              "                     1                                   ...   836 (10.2)\n",
              "oasis                                                 0  ...   31.5 (8.9)\n",
              "oasis_prob                                            0  ...    0.1 (0.1)\n",
              "first_careunit       CCU                              0  ...  1316 (16.1)\n",
              "                     CSRU                                ...    431 (5.3)\n",
              "                     MICU                                ...  3471 (42.4)\n",
              "                     SICU                                ...  1486 (18.1)\n",
              "                     TSICU                               ...  1491 (18.2)\n",
              "\n",
              "[20 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CF_ROxeMWlN",
        "colab_type": "text"
      },
      "source": [
        "It looks like there's a higher rate of hospital mortality (14.0% vs 10.8%) and ICU mortality (10.2% vs 7.8%) on weekends when compared to weekdays.  There are also statistically significant differences between several other important variables, including: admission type, disease severity (OASIS), and the patient's first care unit, suggesting that these groups may be fundamentally different in some way.  Let's explore this a little further."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxCQFro3MWlO",
        "colab_type": "text"
      },
      "source": [
        "# Plot the data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ouamegr7MWlP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "044fac04-a13e-4ffd-a0ec-14c73ea57dbc"
      },
      "source": [
        "# Pivot data to summarise by day\n",
        "dat_dow = data.groupby(['admission_type',\n",
        "                        'inday_icu_seq'])['hospital_expire_flag'].mean().reset_index()\n",
        "\n",
        "dat_dow = dat_dow.pivot(index='inday_icu_seq', \n",
        "                        columns='admission_type', values='hospital_expire_flag')\n",
        "\n",
        "dat_dow"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>admission_type</th>\n",
              "      <th>ELECTIVE</th>\n",
              "      <th>EMERGENCY</th>\n",
              "      <th>URGENT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inday_icu_seq</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.020553</td>\n",
              "      <td>0.124387</td>\n",
              "      <td>0.137931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.019350</td>\n",
              "      <td>0.130315</td>\n",
              "      <td>0.082759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.026549</td>\n",
              "      <td>0.127391</td>\n",
              "      <td>0.112676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.027027</td>\n",
              "      <td>0.133137</td>\n",
              "      <td>0.114504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.026575</td>\n",
              "      <td>0.126612</td>\n",
              "      <td>0.093023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.080247</td>\n",
              "      <td>0.137072</td>\n",
              "      <td>0.162896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.089109</td>\n",
              "      <td>0.146971</td>\n",
              "      <td>0.123596</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "admission_type  ELECTIVE  EMERGENCY    URGENT\n",
              "inday_icu_seq                                \n",
              "0               0.020553   0.124387  0.137931\n",
              "1               0.019350   0.130315  0.082759\n",
              "2               0.026549   0.127391  0.112676\n",
              "3               0.027027   0.133137  0.114504\n",
              "4               0.026575   0.126612  0.093023\n",
              "5               0.080247   0.137072  0.162896\n",
              "6               0.089109   0.146971  0.123596"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woeoHXJ3MWlR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "eb553a96-b901-4af2-f997-071931b9105a"
      },
      "source": [
        "# day_map = {0:'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri', 5:'Sat', 6:'Sun'}\n",
        "ax = dat_dow.plot()\n",
        "ax.set_ylabel(\"Hospital mortality rate\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Hospital mortality rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FJWuAd00jmRQtmScbWXNVykpwf+150J01ITqEF33KInaOrCvZDscqbaFSKyhmu/W1belY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\nWv8PL+fifEWYbacAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWARmoRoMWlT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "10bd0a9e-1d1b-4d15-b902-c3baf7ffc35a"
      },
      "source": [
        "dat_wkd = data.groupby(['admission_type','inday_icu_wkd'])['hospital_expire_flag'].mean().reset_index()\n",
        "dat_wkd = dat_wkd.pivot(index='inday_icu_wkd', columns='admission_type', values='hospital_expire_flag')\n",
        "dat_wkd.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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 th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>admission_type</th>\n",
              "      <th>ELECTIVE</th>\n",
              "      <th>EMERGENCY</th>\n",
              "      <th>URGENT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inday_icu_wkd</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>weekday</th>\n",
              "      <td>0.023732</td>\n",
              "      <td>0.128353</td>\n",
              "      <td>0.108382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weekend</th>\n",
              "      <td>0.083650</td>\n",
              "      <td>0.141909</td>\n",
              "      <td>0.145363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "admission_type  ELECTIVE  EMERGENCY    URGENT\n",
              "inday_icu_wkd                                \n",
              "weekday         0.023732   0.128353  0.108382\n",
              "weekend         0.083650   0.141909  0.145363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vd8xKa_qMWlV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "4934c6db-6689-4c55-8dde-d00305c12443"
      },
      "source": [
        "ax = dat_wkd.plot()\n",
        "ax.set_ylabel(\"Hospital mortality rate\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Hospital mortality rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YIfEsnJpGVloSJ/ePDviXfUVFhQWdABIREhISKCoqCnRTAO8Cz/3AO8B6Vd0k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\ngccYY4xfWeAxxhjjV/8ffBTTslnVWoQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "otYP7AWBMWlX",
        "colab_type": "text"
      },
      "source": [
        "# Model building\n",
        "\n",
        "Let's try to incorporate what we saw above into a very simple model. We will use logistic regression with hospital mortality as our outcome. First an unadjusted estimate, and then we will try to adjust for admission type.\n",
        "\n",
        "The unadjusted analysis should mirror pretty closely what we saw in the one of the tables above. The odds ratio corresponding with 14.0% and 10.8% mortality in the the weekend and weekday groups, respectively, is about 1.35. Performing logistic regression on the same data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UoUx0qm3MWlb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "6ecdeb0e-fcee-421f-b540-d69360be3f37"
      },
      "source": [
        "# R style syntax\n",
        "simple_glm = smf.glm('hospital_expire_flag ~ C(inday_icu_wkd)', \n",
        "                     data=data, family=sm.families.Binomial()).fit()\n",
        "simple_glm.summary2()\n",
        "\n",
        "# Alternative syntax\n",
        "# y = data.hospital_expire_flag\n",
        "# X = sm.tools.add_constant(data.inday_icu_wkd.factorize()[0])\n",
        "# simple_glm = sm.GLM(y, X, family=sm.families.Binomial()).fit()\n",
        "# simple_glm.summary2()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td>Model:</td>                 <td>GLM</td>              <td>AIC:</td>        <td>27417.0809</td> \n",
              "</tr>\n",
              "<tr>\n",
              "    <td>Link Function:</td>            <td>logit</td>             <td>BIC:</td>       <td>-379735.1515</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <td>Dependent Variable:</td> <td>hospital_expire_flag</td> <td>Log-Likelihood:</td>    <td>-13707.</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "         <td>Date:</td>          <td>2020-01-17 20:53</td>      <td>LL-Null:</td>        <td>-13738.</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "   <td>No. Observations:</td>          <td>38558</td>           <td>Deviance:</td>       <td>27413.</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "       <td>Df Model:</td>                <td>1</td>           <td>Pearson chi2:</td>    <td>3.86e+04</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "     <td>Df Residuals:</td>            <td>38556</td>            <td>Scale:</td>         <td>1.0000</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "        <td>Method:</td>               <td>IRLS</td>                <td></td>               <td></td>      \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "               <td></td>                <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>      <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>                   <td>-2.1128</td>  <td>0.0185</td>  <td>-114.2022</td> <td>0.0000</td> <td>-2.1490</td> <td>-2.0765</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(inday_icu_wkd)[T.weekend]</th> <td>0.2992</td>   <td>0.0368</td>   <td>8.1298</td>   <td>0.0000</td> <td>0.2271</td>  <td>0.3713</td> \n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary2.Summary'>\n",
              "\"\"\"\n",
              "                      Results: Generalized linear model\n",
              "=============================================================================\n",
              "Model:                  GLM                    AIC:              27417.0809  \n",
              "Link Function:          logit                  BIC:              -379735.1515\n",
              "Dependent Variable:     hospital_expire_flag   Log-Likelihood:   -13707.     \n",
              "Date:                   2020-01-17 20:53       LL-Null:          -13738.     \n",
              "No. Observations:       38558                  Deviance:         27413.      \n",
              "Df Model:               1                      Pearson chi2:     3.86e+04    \n",
              "Df Residuals:           38556                  Scale:            1.0000      \n",
              "Method:                 IRLS                                                 \n",
              "-----------------------------------------------------------------------------\n",
              "                             Coef.  Std.Err.     z     P>|z|   [0.025  0.975]\n",
              "-----------------------------------------------------------------------------\n",
              "Intercept                   -2.1128   0.0185 -114.2022 0.0000 -2.1490 -2.0765\n",
              "C(inday_icu_wkd)[T.weekend]  0.2992   0.0368    8.1298 0.0000  0.2271  0.3713\n",
              "=============================================================================\n",
              "\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NcJqsAMMWle",
        "colab_type": "text"
      },
      "source": [
        "...yields the same results. The coefficient shown above for weekend is on the log scale, so when we exponentiate it, we get the odds-ratio: `exp(0.2992) = 1.35`. So, looking at these crude rates and odds ratios, we can see that patients admitted on a weekend have about a 35% increase in the odds of dying in the hospital when compared to those on a weekday. This effect is statistically significant (p<0.001). \n",
        "\n",
        "Are we done?\n",
        "\n",
        "I hope not. We saw from the tables and figures above, there is likely some confounding and maybe even effect modification happening. Next let''s look at admission type and weekend ICU admission in the same model. There are two such models we could consider. \n",
        "\n",
        "The first adjusts for admission type, but assumes that the effect of weekend admission is the same regardless if the patient is of any of the admission types. The second one adjusts for admission type, but then allows the effect of weekend ICU admission to vary across the different levels of admission type. \n",
        "\n",
        "The first type of model would be able to account for confounding (when a nuisance variable is associated with both the outcome and the exposure/variable of interest), while the second permits what is called effect modification or a statistical interaction. \n",
        "\n",
        "Interactions are sometimes difficult to understand, but if ignored, can lead to incorrect conclusions about the effect of one or more of the variables. In this example, we fit both models, output estimates of the log-odds ratios, and perform a hypothesis test which evaluates the statistical significance of dropping one of the variables. Below is the resulting output:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhHdvAN2MWlf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "a0016b22-f1c9-41da-ced4-b524e1fe266e"
      },
      "source": [
        "# Without effect modification\n",
        "adj_glm = smf.glm('hospital_expire_flag ~ C(inday_icu_wkd) + C(admission_type)', \n",
        "                     data=data, family=sm.families.Binomial()).fit()\n",
        "adj_glm.summary2()\n",
        "# drop1(adj.glm,test=\"Chisq\")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td>Model:</td>                 <td>GLM</td>              <td>AIC:</td>        <td>26729.2870</td> \n",
              "</tr>\n",
              "<tr>\n",
              "    <td>Link Function:</td>            <td>logit</td>             <td>BIC:</td>       <td>-380405.8255</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <td>Dependent Variable:</td> <td>hospital_expire_flag</td> <td>Log-Likelihood:</td>    <td>-13361.</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "         <td>Date:</td>          <td>2020-01-17 20:53</td>      <td>LL-Null:</td>        <td>-13738.</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "   <td>No. Observations:</td>          <td>38558</td>           <td>Deviance:</td>       <td>26721.</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "       <td>Df Model:</td>                <td>3</td>           <td>Pearson chi2:</td>    <td>3.85e+04</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "     <td>Df Residuals:</td>            <td>38554</td>            <td>Scale:</td>         <td>1.0000</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "        <td>Method:</td>               <td>IRLS</td>                <td></td>               <td></td>      \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "                 <td></td>                 <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>                      <td>-3.6173</td>  <td>0.0801</td>  <td>-45.1365</td> <td>0.0000</td> <td>-3.7743</td> <td>-3.4602</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(inday_icu_wkd)[T.weekend]</th>    <td>0.1445</td>   <td>0.0372</td>   <td>3.8883</td>  <td>0.0001</td> <td>0.0716</td>  <td>0.2173</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(admission_type)[T.EMERGENCY]</th> <td>1.6943</td>   <td>0.0822</td>   <td>20.6089</td> <td>0.0000</td> <td>1.5332</td>  <td>1.8555</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(admission_type)[T.URGENT]</th>    <td>1.5881</td>   <td>0.1231</td>   <td>12.9032</td> <td>0.0000</td> <td>1.3469</td>  <td>1.8293</td> \n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary2.Summary'>\n",
              "\"\"\"\n",
              "                       Results: Generalized linear model\n",
              "===============================================================================\n",
              "Model:                  GLM                     AIC:               26729.2870  \n",
              "Link Function:          logit                   BIC:               -380405.8255\n",
              "Dependent Variable:     hospital_expire_flag    Log-Likelihood:    -13361.     \n",
              "Date:                   2020-01-17 20:53        LL-Null:           -13738.     \n",
              "No. Observations:       38558                   Deviance:          26721.      \n",
              "Df Model:               3                       Pearson chi2:      3.85e+04    \n",
              "Df Residuals:           38554                   Scale:             1.0000      \n",
              "Method:                 IRLS                                                   \n",
              "-------------------------------------------------------------------------------\n",
              "                                Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
              "-------------------------------------------------------------------------------\n",
              "Intercept                      -3.6173   0.0801 -45.1365 0.0000 -3.7743 -3.4602\n",
              "C(inday_icu_wkd)[T.weekend]     0.1445   0.0372   3.8883 0.0001  0.0716  0.2173\n",
              "C(admission_type)[T.EMERGENCY]  1.6943   0.0822  20.6089 0.0000  1.5332  1.8555\n",
              "C(admission_type)[T.URGENT]     1.5881   0.1231  12.9032 0.0000  1.3469  1.8293\n",
              "===============================================================================\n",
              "\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FC4bS99HMWlh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "605a7d66-8389-4f98-cffa-2e0319f74bde"
      },
      "source": [
        "# With effect modification\n",
        "adj_glm_int = smf.glm('hospital_expire_flag ~ C(inday_icu_wkd) * C(admission_type)', \n",
        "                     data=data, family=sm.families.Binomial()).fit()\n",
        "adj_glm_int.summary2()\n",
        "# drop1(adj.glm,test=\"Chisq\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td>Model:</td>                 <td>GLM</td>              <td>AIC:</td>        <td>26712.7150</td> \n",
              "</tr>\n",
              "<tr>\n",
              "    <td>Link Function:</td>            <td>logit</td>             <td>BIC:</td>       <td>-380405.2776</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <td>Dependent Variable:</td> <td>hospital_expire_flag</td> <td>Log-Likelihood:</td>    <td>-13350.</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "         <td>Date:</td>          <td>2020-01-17 20:54</td>      <td>LL-Null:</td>        <td>-13738.</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "   <td>No. Observations:</td>          <td>38558</td>           <td>Deviance:</td>       <td>26701.</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "       <td>Df Model:</td>                <td>5</td>           <td>Pearson chi2:</td>    <td>3.86e+04</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "     <td>Df Residuals:</td>            <td>38552</td>            <td>Scale:</td>         <td>1.0000</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "        <td>Method:</td>               <td>IRLS</td>                <td></td>               <td></td>      \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "                               <td></td>                               <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>                                                  <td>-3.7169</td>  <td>0.0862</td>  <td>-43.1428</td> <td>0.0000</td> <td>-3.8858</td> <td>-3.5481</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(inday_icu_wkd)[T.weekend]</th>                                <td>1.3232</td>   <td>0.2388</td>   <td>5.5409</td>  <td>0.0000</td> <td>0.8551</td>  <td>1.7912</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(admission_type)[T.EMERGENCY]</th>                             <td>1.8013</td>   <td>0.0883</td>   <td>20.3997</td> <td>0.0000</td> <td>1.6283</td>  <td>1.9744</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(admission_type)[T.URGENT]</th>                                <td>1.6095</td>   <td>0.1496</td>   <td>10.7598</td> <td>0.0000</td> <td>1.3164</td>  <td>1.9027</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(inday_icu_wkd)[T.weekend]:C(admission_type)[T.EMERGENCY]</th> <td>-1.2071</td>  <td>0.2418</td>   <td>-4.9911</td> <td>0.0000</td> <td>-1.6811</td> <td>-0.7331</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C(inday_icu_wkd)[T.weekend]:C(admission_type)[T.URGENT]</th>    <td>-0.9872</td>  <td>0.3036</td>   <td>-3.2521</td> <td>0.0011</td> <td>-1.5822</td> <td>-0.3922</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary2.Summary'>\n",
              "\"\"\"\n",
              "                                     Results: Generalized linear model\n",
              "===========================================================================================================\n",
              "Model:                            GLM                              AIC:                        26712.7150  \n",
              "Link Function:                    logit                            BIC:                        -380405.2776\n",
              "Dependent Variable:               hospital_expire_flag             Log-Likelihood:             -13350.     \n",
              "Date:                             2020-01-17 20:54                 LL-Null:                    -13738.     \n",
              "No. Observations:                 38558                            Deviance:                   26701.      \n",
              "Df Model:                         5                                Pearson chi2:               3.86e+04    \n",
              "Df Residuals:                     38552                            Scale:                      1.0000      \n",
              "Method:                           IRLS                                                                     \n",
              "-----------------------------------------------------------------------------------------------------------\n",
              "                                                            Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
              "-----------------------------------------------------------------------------------------------------------\n",
              "Intercept                                                  -3.7169   0.0862 -43.1428 0.0000 -3.8858 -3.5481\n",
              "C(inday_icu_wkd)[T.weekend]                                 1.3232   0.2388   5.5409 0.0000  0.8551  1.7912\n",
              "C(admission_type)[T.EMERGENCY]                              1.8013   0.0883  20.3997 0.0000  1.6283  1.9744\n",
              "C(admission_type)[T.URGENT]                                 1.6095   0.1496  10.7598 0.0000  1.3164  1.9027\n",
              "C(inday_icu_wkd)[T.weekend]:C(admission_type)[T.EMERGENCY] -1.2071   0.2418  -4.9911 0.0000 -1.6811 -0.7331\n",
              "C(inday_icu_wkd)[T.weekend]:C(admission_type)[T.URGENT]    -0.9872   0.3036  -3.2521 0.0011 -1.5822 -0.3922\n",
              "===========================================================================================================\n",
              "\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5IkTbBnMWlj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "3cef75bb-3084-439c-cd23-269bb49ae8c4"
      },
      "source": [
        "# Create data structure to hold odds of hospital death\n",
        "def expand_grid(data_dict):\n",
        "    rows = itertools.product(*data_dict.values())\n",
        "    return pd.DataFrame.from_records(rows, columns=data_dict.keys())\n",
        "\n",
        "weekend_grid = expand_grid({'inday_icu_wkd': ['weekday', 'weekend'],\n",
        "                            'admission_type': ['ELECTIVE', 'EMERGENCY', 'URGENT']})\n",
        "\n",
        "weekend_grid"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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 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>inday_icu_wkd</th>\n",
              "      <th>admission_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>weekday</td>\n",
              "      <td>ELECTIVE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>weekday</td>\n",
              "      <td>EMERGENCY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>weekday</td>\n",
              "      <td>URGENT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>weekend</td>\n",
              "      <td>ELECTIVE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>weekend</td>\n",
              "      <td>EMERGENCY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>weekend</td>\n",
              "      <td>URGENT</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  inday_icu_wkd admission_type\n",
              "0       weekday       ELECTIVE\n",
              "1       weekday      EMERGENCY\n",
              "2       weekday         URGENT\n",
              "3       weekend       ELECTIVE\n",
              "4       weekend      EMERGENCY\n",
              "5       weekend         URGENT"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HBvyZbBXMWll",
        "colab_type": "text"
      },
      "source": [
        "In the first model (no interaction), we see that although the effect of weekend is almost halved, it remains statistically significant, after adjusting for admission type (p<0.001).\n",
        "\n",
        "In the second model, we are primarily interested in the significance of the interaction.  We can see when assessed with the `drop1` function, the interaction (`weekend:admission_type`) is statistically significant (p<0.001), suggesting that the effect of weekend may be different depending on which hospital admission type you are.  How exactly to interpret this:\n",
        "\n",
        "One way of looking at this complexity is by computing the odds ratio in each of the levels of admission type.  We can do this using the `predict` function, which by default outputs the log-odds of death.  If for each hospital admission type, we calculate the log odds of death for each of the levels of weekend,"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ucLxfa4KMWlm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prob2logodds(prob):\n",
        "    odds = prob / (1 - prob)\n",
        "    logodds = np.log(odds)\n",
        "    return logodds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "wEPXVNtFMWlo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "65ef31df-5d7a-48a2-e1c5-1dd0f132c3d0"
      },
      "source": [
        "weekend_grid['predict'] = adj_glm_int.predict(weekend_grid[['inday_icu_wkd','admission_type']])\n",
        "weekend_grid['log_odds'] = prob2logodds(weekend_grid['predict'])\n",
        "weekend_grid.set_index(['inday_icu_wkd','admission_type'], inplace=True)\n",
        "weekend_grid"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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 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></th>\n",
              "      <th>predict</th>\n",
              "      <th>log_odds</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inday_icu_wkd</th>\n",
              "      <th>admission_type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">weekday</th>\n",
              "      <th>ELECTIVE</th>\n",
              "      <td>0.023732</td>\n",
              "      <td>-3.716925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EMERGENCY</th>\n",
              "      <td>0.128353</td>\n",
              "      <td>-1.915596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URGENT</th>\n",
              "      <td>0.108382</td>\n",
              "      <td>-2.107381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">weekend</th>\n",
              "      <th>ELECTIVE</th>\n",
              "      <td>0.083650</td>\n",
              "      <td>-2.393754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EMERGENCY</th>\n",
              "      <td>0.141909</td>\n",
              "      <td>-1.799525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URGENT</th>\n",
              "      <td>0.145363</td>\n",
              "      <td>-1.771439</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               predict  log_odds\n",
              "inday_icu_wkd admission_type                    \n",
              "weekday       ELECTIVE        0.023732 -3.716925\n",
              "              EMERGENCY       0.128353 -1.915596\n",
              "              URGENT          0.108382 -2.107381\n",
              "weekend       ELECTIVE        0.083650 -2.393754\n",
              "              EMERGENCY       0.141909 -1.799525\n",
              "              URGENT          0.145363 -1.771439"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGmTCA6TMWlq",
        "colab_type": "text"
      },
      "source": [
        "We can now compute the log odds ratio ($log(OR) =  logOdds_{weekend} - logOdds_{weekday}$), and exponentiate to get the odds ratio:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1iIeZmheMWlr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "51fce423-a1e7-473e-a9c1-81ddb4754b85"
      },
      "source": [
        "diff_grid = weekend_grid.loc['weekend']['log_odds'] - weekend_grid.loc['weekday']['log_odds']\n",
        "np.exp(diff_grid)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "admission_type\n",
              "ELECTIVE     3.755307\n",
              "EMERGENCY    1.123076\n",
              "URGENT       1.399257\n",
              "Name: log_odds, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxlSkP2XMWlt",
        "colab_type": "text"
      },
      "source": [
        "So, this mirrors what we saw above. While there may be differences between EMERGENCY and URGENT admission types, an ELECTIVE admission occurring on a weekend has an odds of mortality almost four times that of an ELECTIVE admission on a weekday. This seems particularly odd -- patients usually do not get admitted to a hospital electively on a weekend.\n",
        "\n",
        "What do you think?\n",
        "\n",
        "- Do patients admitted on a weekend have a higher rate of mortality than those admitted during the week?\n",
        "- Who is most affected, if at all?\n",
        "- What factors can you rule out might be causing this effect? e.g., is it because the patients are simply sicker on a weekend? Are they more likely to have complications?\n",
        "\n",
        "Looking forward to see what you guys come up with!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWW22eBvMWlt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}