EP1_intro_mimiciv.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/glpappa/ML4Health/blob/main/EP1_intro_mimiciv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "metadata": {
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      "source": [
        "# Exercício Prático 1 \n",
        "**Introdução a base MIMIC-IV**\n",
        "\n",
        "Esse exercício será avaliado apenas para a graduação.No entanto, aconselhamos os outros alunos a seguí-lo para se acostumarem com a base de dados, o que pode facilitar a execução do próximo trabalho.\n",
        "\n",
        "Aconselhamos vocês a seguirem o fluxo do notebook, uma vez que algumas tarefas são encadeadas. Todas as partes que precisam ser implmentadas estão maracada como \"Análises a serem implementadas\" ou simplesmente \"Análises\".\n",
        "\n",
        "---\n",
        "\n",
        "**ATENÇÃO**\n",
        "\n",
        "- Para executar esse notebook, você precisa ter acesso às bases do MIMIC-IV no [BigQuery](https://mimic.mit.edu/docs/gettingstarted/cloud/bigquery/).\n",
        "- Para executar esse notebook local, você precisa instalar o [gcloud-cli](https://cloud.google.com/sdk/docs/install?hl=pt-br) e inicializá-lo.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cc87dfec-c9de-4905-8d95-18cd633e8a5e",
      "metadata": {
        "id": "cc87dfec-c9de-4905-8d95-18cd633e8a5e"
      },
      "source": [
        "## Carregar pacotes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9916279c-9837-4b2e-83fe-a977b1bec9c5",
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        "tags": [],
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      "source": [
        "from google.cloud import bigquery\n",
        "from collections import OrderedDict\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a5efd991-00dd-4c23-9e70-6fead883eb78",
      "metadata": {
        "id": "a5efd991-00dd-4c23-9e70-6fead883eb78"
      },
      "source": [
        "**Rode o código abaixo somente se estiver no colab.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7e8d2341-b751-4e5e-8337-a3fc09c9c2e1",
      "metadata": {
        "tags": [],
        "id": "7e8d2341-b751-4e5e-8337-a3fc09c9c2e1"
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      "outputs": [],
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "# from google.colab import data_table\n",
        "# data_table.enable_dataframe_formatter()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ccd0775b-5e82-4149-8a31-fb7c46eb7d35",
      "metadata": {
        "id": "ccd0775b-5e82-4149-8a31-fb7c46eb7d35"
      },
      "source": [
        "## Variáveis globais"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "db6f939b-9890-4129-a819-16d8e5924ffc",
      "metadata": {
        "tags": [],
        "id": "db6f939b-9890-4129-a819-16d8e5924ffc"
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      "outputs": [],
      "source": [
        "projeto = 'mimic-ml-01' # Project ID inserted based on the query results selected to explore\n",
        "location = 'US' # Location inserted based on the query results selected to explore\n",
        "\n",
        "dataset_hosp = 'physionet-data.mimiciv_hosp'\n",
        "dataset_icu = 'physionet-data.mimiciv_icu'\n",
        "dataset_ed = 'physionet-data.mimiciv_ed'\n",
        "dataset_note = 'physionet-data.mimiciv_note'\n",
        "dataset_cxr = 'physionet-data.mimic_cxr'\n",
        "\n",
        "paciente = 10012853\n",
        "admissao = 27882036"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a7c867e6-95e0-401c-9321-3cf3c8c61607",
      "metadata": {
        "id": "a7c867e6-95e0-401c-9321-3cf3c8c61607"
      },
      "source": [
        "## Funções auxiliares"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "def get_tabela(projeto_id:str, dataset_id: str, tabela_id: str, atributos_str:str, where_str:str = '') -> pd.DataFrame:\n",
        "    sql = 'SELECT ' + atributos_str + ' FROM `' + dataset_id + '.' + tabela_id + '`'\n",
        "    if where_str != '':\n",
        "        sql += ' WHERE ' + where_str\n",
        "    sql += ';'\n",
        "    print(sql)\n",
        "    return(pd.read_gbq(sql, projeto_id))\n",
        "\n",
        "def get_lista_tabelas(dataset_id: str) -> list:\n",
        "    lista_tabelas = cliente.list_tables(dataset_id)\n",
        "    lista = [tabela.table_id for tabela in lista_tabelas]\n",
        "    return(lista)\n",
        "\n",
        "def imprimir_lista_tabelas(dataset_id: str) -> None:\n",
        "    lista_tabelas = cliente.list_tables(dataset_id)\n",
        "    print(\"Tabelas da base de dados '{}':\".format(dataset_id))\n",
        "    for i, tabela in enumerate(lista_tabelas):\n",
        "        print(\"\\t{}- {}.{}.{}\".format(i+1, tabela.project, tabela.dataset_id, tabela.table_id))\n",
        "        \n",
        "def imprimir_lista_atributos(dataset_id: str, tabela_id: str) -> pd.DataFrame:\n",
        "    tabela = cliente.get_table(dataset_id + '.' + tabela_id)\n",
        "    nomes = [] \n",
        "    tipos = []\n",
        "    modos = []\n",
        "    for coluna in tabela.schema:\n",
        "        nomes.append(coluna.name)\n",
        "        tipos.append(coluna.field_type)\n",
        "        modos.append(coluna.mode)\n",
        "    df = pd.DataFrame({'Nome': nomes, 'Tipo do campo': tipos, 'Modo': modos})\n",
        "    display(df)\n"
      ]
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    {
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      "metadata": {
        "id": "94c41f1f-3718-41ee-87f5-70026df22dd7"
      },
      "source": [
        "## Conectar ao bigquery"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "cliente = bigquery.Client(project=projeto, location=location)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Listagem das tabelas\n",
        "A MIMIC-IV é divida em 4 bases de dados: MIMIC-HOSP, MIMIC-ED, MIMIC-ICU, MIMIC-CXR.\n",
        "Abaixo, listamos as tabelas de cada uma das bases.\n",
        "\n"
      ],
      "metadata": {
        "id": "Z9Oq45bmKOzR"
      },
      "id": "Z9Oq45bmKOzR"
    },
    {
      "cell_type": "markdown",
      "id": "aeb11ca7-b2ba-40b0-ae40-572b3ab4596a",
      "metadata": {
        "id": "aeb11ca7-b2ba-40b0-ae40-572b3ab4596a"
      },
      "source": [
        "## MIMIC-HOSP\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7dad806f-c2ab-4414-9749-e0999da463c9",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dad806f-c2ab-4414-9749-e0999da463c9",
        "outputId": "4055a695-be3f-4f40-8db0-7355f75729d3"
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      "outputs": [
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          "text": [
            "Tabelas da base de dados 'physionet-data.mimiciv_hosp':\n",
            "\t1- physionet-data.mimiciv_hosp.admissions\n",
            "\t2- physionet-data.mimiciv_hosp.d_hcpcs\n",
            "\t3- physionet-data.mimiciv_hosp.d_icd_diagnoses\n",
            "\t4- physionet-data.mimiciv_hosp.d_icd_procedures\n",
            "\t5- physionet-data.mimiciv_hosp.d_labitems\n",
            "\t6- physionet-data.mimiciv_hosp.diagnoses_icd\n",
            "\t7- physionet-data.mimiciv_hosp.drgcodes\n",
            "\t8- physionet-data.mimiciv_hosp.emar\n",
            "\t9- physionet-data.mimiciv_hosp.emar_detail\n",
            "\t10- physionet-data.mimiciv_hosp.hcpcsevents\n",
            "\t11- physionet-data.mimiciv_hosp.labevents\n",
            "\t12- physionet-data.mimiciv_hosp.microbiologyevents\n",
            "\t13- physionet-data.mimiciv_hosp.omr\n",
            "\t14- physionet-data.mimiciv_hosp.patients\n",
            "\t15- physionet-data.mimiciv_hosp.pharmacy\n",
            "\t16- physionet-data.mimiciv_hosp.poe\n",
            "\t17- physionet-data.mimiciv_hosp.poe_detail\n",
            "\t18- physionet-data.mimiciv_hosp.prescriptions\n",
            "\t19- physionet-data.mimiciv_hosp.procedures_icd\n",
            "\t20- physionet-data.mimiciv_hosp.provider\n",
            "\t21- physionet-data.mimiciv_hosp.services\n",
            "\t22- physionet-data.mimiciv_hosp.transfers\n"
          ]
        }
      ],
      "source": [
        "imprimir_lista_tabelas(dataset_hosp)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MIMIC-ED"
      ],
      "metadata": {
        "id": "pFjYgZfCLSJI"
      },
      "id": "pFjYgZfCLSJI"
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir_lista_tabelas(dataset_ed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFTX66naK_10",
        "outputId": "dd2e0f06-908d-4f7e-a802-8a8c828045c8"
      },
      "id": "jFTX66naK_10",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tabelas da base de dados 'physionet-data.mimiciv_ed':\n",
            "\t1- physionet-data.mimiciv_ed.diagnosis\n",
            "\t2- physionet-data.mimiciv_ed.edstays\n",
            "\t3- physionet-data.mimiciv_ed.medrecon\n",
            "\t4- physionet-data.mimiciv_ed.pyxis\n",
            "\t5- physionet-data.mimiciv_ed.triage\n",
            "\t6- physionet-data.mimiciv_ed.vitalsign\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MIMIC-ICU"
      ],
      "metadata": {
        "id": "xNsR-5sxLZwS"
      },
      "id": "xNsR-5sxLZwS"
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir_lista_tabelas(dataset_icu)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUele7-uLCXa",
        "outputId": "10866673-9601-4da7-bfbc-5938b1fc75f5"
      },
      "id": "tUele7-uLCXa",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tabelas da base de dados 'physionet-data.mimiciv_icu':\n",
            "\t1- physionet-data.mimiciv_icu.caregiver\n",
            "\t2- physionet-data.mimiciv_icu.chartevents\n",
            "\t3- physionet-data.mimiciv_icu.d_items\n",
            "\t4- physionet-data.mimiciv_icu.datetimeevents\n",
            "\t5- physionet-data.mimiciv_icu.icustays\n",
            "\t6- physionet-data.mimiciv_icu.ingredientevents\n",
            "\t7- physionet-data.mimiciv_icu.inputevents\n",
            "\t8- physionet-data.mimiciv_icu.outputevents\n",
            "\t9- physionet-data.mimiciv_icu.procedureevents\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MIMIC-CXR"
      ],
      "metadata": {
        "id": "sPeM6FVyLcbd"
      },
      "id": "sPeM6FVyLcbd"
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir_lista_tabelas(dataset_cxr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dO5myUQLEva",
        "outputId": "7b708c28-cf63-4ca6-be52-9ca64ced6436"
      },
      "id": "0dO5myUQLEva",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tabelas da base de dados 'physionet-data.mimic_cxr':\n",
            "\t1- physionet-data.mimic_cxr.chexpert\n",
            "\t2- physionet-data.mimic_cxr.dicom_metadata_string\n",
            "\t3- physionet-data.mimic_cxr.record_list\n",
            "\t4- physionet-data.mimic_cxr.study_list\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imprimir_lista_tabelas(dataset_note)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VFxMpFMxLHil",
        "outputId": "dda049fd-df2c-450b-e826-78e7a147c360"
      },
      "id": "VFxMpFMxLHil",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tabelas da base de dados 'physionet-data.mimiciv_note':\n",
            "\t1- physionet-data.mimiciv_note.discharge\n",
            "\t2- physionet-data.mimiciv_note.discharge_detail\n",
            "\t3- physionet-data.mimiciv_note.radiology\n",
            "\t4- physionet-data.mimiciv_note.radiology_detail\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b37e5541-c3c8-419b-939e-2174ee6e808d",
      "metadata": {
        "tags": [],
        "id": "b37e5541-c3c8-419b-939e-2174ee6e808d"
      },
      "source": [
        "# Caracterização populacional\n",
        "Nessa seção, são apresentadas algumas caracterizações considerando a população disponível na base."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Dados demográficos\n",
        "- As tabelas que possuem dados demográficos são *admissions*, *patients* e *edstays*"
      ],
      "metadata": {
        "id": "EEoF8rwDLt6u"
      },
      "id": "EEoF8rwDLt6u"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Investigando a tabela *patients*"
      ],
      "metadata": {
        "id": "8Z9RSMN4OYEV"
      },
      "id": "8Z9RSMN4OYEV"
    },
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      "outputs": [
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          "output_type": "display_data",
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              "                Nome Tipo do campo      Modo\n",
              "0         subject_id       INTEGER  REQUIRED\n",
              "1             gender        STRING  REQUIRED\n",
              "2         anchor_age       INTEGER  NULLABLE\n",
              "3        anchor_year       INTEGER  REQUIRED\n",
              "4  anchor_year_group        STRING  REQUIRED\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1d14af5e-c214-49c9-8b53-8a487ef484aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1d14af5e-c214-49c9-8b53-8a487ef484aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "imprimir_lista_atributos(dataset_hosp, 'patients')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c88f1ef-7666-44e6-9e5b-38e0f2389625",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5c88f1ef-7666-44e6-9e5b-38e0f2389625",
        "outputId": "a5078f27-d499-4792-dff8-2e26e166c264"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SELECT * FROM `physionet-data.mimiciv_hosp.patients`;\n"
          ]
        }
      ],
      "source": [
        "pacientes = get_tabela(projeto, dataset_hosp, 'patients', '*', '') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4602add3-cdac-4409-bc42-30d9df9e07a5",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4602add3-cdac-4409-bc42-30d9df9e07a5",
        "outputId": "c933fb83-1bcc-4ff4-98e2-fdc14749c3aa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   subject_id gender  anchor_age  anchor_year anchor_year_group  dod\n",
              "0    10078138      F          18         2110       2017 - 2019  NaT\n",
              "1    10180372      M          18         2110       2008 - 2010  NaT\n",
              "2    10686175      M          18         2110       2011 - 2013  NaT\n",
              "3    10851602      F          18         2110       2014 - 2016  NaT\n",
              "4    10902424      F          18         2110       2017 - 2019  NaT"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae242e86-a026-4b67-b84e-4f0ff81cecee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "</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>gender</th>\n",
              "      <th>anchor_age</th>\n",
              "      <th>anchor_year</th>\n",
              "      <th>anchor_year_group</th>\n",
              "      <th>dod</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10078138</td>\n",
              "      <td>F</td>\n",
              "      <td>18</td>\n",
              "      <td>2110</td>\n",
              "      <td>2017 - 2019</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10180372</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>2110</td>\n",
              "      <td>2008 - 2010</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10686175</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>2110</td>\n",
              "      <td>2011 - 2013</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10851602</td>\n",
              "      <td>F</td>\n",
              "      <td>18</td>\n",
              "      <td>2110</td>\n",
              "      <td>2014 - 2016</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10902424</td>\n",
              "      <td>F</td>\n",
              "      <td>18</td>\n",
              "      <td>2110</td>\n",
              "      <td>2017 - 2019</td>\n",
              "      <td>NaT</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae242e86-a026-4b67-b84e-4f0ff81cecee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae242e86-a026-4b67-b84e-4f0ff81cecee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae242e86-a026-4b67-b84e-4f0ff81cecee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(pacientes.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "56d74b09-1312-4b9a-a01e-3a4620b57688",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56d74b09-1312-4b9a-a01e-3a4620b57688",
        "outputId": "2cb1194a-a31c-44cc-917c-079bda5df481"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(299712, 6)\n",
            "Foram registrados 299712 pacientes no hospital.\n"
          ]
        }
      ],
      "source": [
        "num_pacientes = pacientes['subject_id'].unique().size\n",
        "print('{}'.format(pacientes.shape))\n",
        "print('Foram registrados ' + str(num_pacientes) + ' pacientes no hospital.')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "624fe8a4-b827-4ec2-8628-e7ebb9aaf885",
      "metadata": {
        "tags": [],
        "id": "624fe8a4-b827-4ec2-8628-e7ebb9aaf885"
      },
      "source": [
        "#### Distribuição da variável idade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "85c72ab7-63bf-451a-baba-4a23902f405e",
      "metadata": {
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "85c72ab7-63bf-451a-baba-4a23902f405e",
        "outputId": "e296c0d0-b0c6-45b7-f7d0-f92596ea026e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "idade_pacientes = pacientes[['subject_id','anchor_age']].groupby('anchor_age').count().reset_index()\n",
        "lista_idades = idade_pacientes['anchor_age'].to_list()\n",
        "for i in range(18,92):\n",
        "    if i not in lista_idades:\n",
        "        idade_pacientes = pd.concat([idade_pacientes, pd.DataFrame({'anchor_age': [i], 'subject_id': [0]})])\n",
        "idade_pacientes = idade_pacientes.sort_values('anchor_age').set_index('anchor_age')\n",
        "\n",
        "ax = idade_pacientes.plot(kind='bar', figsize=(15, 10), legend='', title='Distribuição do número de pacientes por idade')\n",
        "ax.set_xlabel(\"anchor_age\", fontsize=12)\n",
        "ax.set_ylabel(\"total de pacientes\", fontsize=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99740ee3-baa0-4ebb-a0de-42ef42315ae3",
      "metadata": {
        "tags": [],
        "id": "99740ee3-baa0-4ebb-a0de-42ef42315ae3"
      },
      "source": [
        "#### Análises a serem implementadas\n",
        "\n",
        "Implemente cada análise em uma célula diferente, e sempre coloque o título do que está sendo analisado, conforme exemplo para variável gênero abaixo.\n",
        "\n",
        "\n",
        "- Faça gráficos da distribuição de gênero (gender), seguro (insurance), estado civil (marital_status), raça (race). Esses dados estão na tabela *admissions*.\n",
        "- Faça o gráfico da distribuição do meio de transporte de chegada (arrival_transport) no departamento de emergência e do local onde o paciente foi encaminhado (disposition). Esses dados estão na tabela *edstays*.\n",
        "- Quantos dos atendimentos registrados no hospital são encaminhados para o departamento de emergência? (*admissions* versus *edstays*)\n",
        "- Que porcentagem de pacientes vai à óbito no hospital?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Distribuição da variável gênero\n",
        "\n"
      ],
      "metadata": {
        "id": "0G1xLyXZOF8Z"
      },
      "id": "0G1xLyXZOF8Z"
    },
    {
      "cell_type": "code",
      "source": [
        "# código para distribuição de gênero"
      ],
      "metadata": {
        "id": "Qc_Bsw_iaoEx"
      },
      "id": "Qc_Bsw_iaoEx",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Dados administrativos"
      ],
      "metadata": {
        "id": "amXfSZ5QG9tA"
      },
      "id": "amXfSZ5QG9tA"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Investigando a tabela *services*"
      ],
      "metadata": {
        "id": "TNqX9LEAzQNo"
      },
      "id": "TNqX9LEAzQNo"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Análises a serem implementadas\n",
        "\n",
        "* Repita o processo realizado na tabela *patients* para a tabela *services*. \n",
        "* Identifique quais os 5 serviços mais utilizados pelos pacientes. "
      ],
      "metadata": {
        "id": "SUVobacKzd9G"
      },
      "id": "SUVobacKzd9G"
    },
    {
      "cell_type": "code",
      "source": [
        "# Escreva seu código aqui."
      ],
      "metadata": {
        "id": "G4dOfsFB093k"
      },
      "id": "G4dOfsFB093k",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Sinais vitais"
      ],
      "metadata": {
        "id": "MuplMKICY832"
      },
      "id": "MuplMKICY832"
    },
    {
      "cell_type": "code",
      "source": [
        "sinais_vitais = get_tabela(projeto, dataset_ed, 'vitalsign', '*', '') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMai91aZZPCL",
        "outputId": "b9750414-a473-43ff-9b18-e0b602fdf0bb"
      },
      "id": "gMai91aZZPCL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SELECT * FROM `physionet-data.mimiciv_ed.vitalsign`;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sinais_vitais.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73bMh9XYaurm",
        "outputId": "8d8eb248-1c9b-4e7f-fb22-5e0dc28af315"
      },
      "id": "73bMh9XYaurm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1564610, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sinais_vitais.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1jG_7vCKaxVC",
        "outputId": "5840a375-7244-4c8f-d110-8e269cbc738c"
      },
      "id": "1jG_7vCKaxVC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   subject_id   stay_id           charttime   temperature      heartrate  \\\n",
              "0    10014967  36889300 2198-09-17 09:01:00  96.400000000   70.000000000   \n",
              "1    10015057  34668050 2151-04-15 01:10:00  97.800000000   69.000000000   \n",
              "2    10023708  32140682 2144-05-18 09:06:00  97.100000000   63.000000000   \n",
              "3    10036086  39396729 2206-03-05 23:59:00          None  101.000000000   \n",
              "4    10036086  39396729 2206-03-06 00:00:00          None  102.000000000   \n",
              "\n",
              "       resprate         o2sat  sbp  dbp               rhythm  pain  \n",
              "0  30.000000000  95.000000000  179  103                 None    10  \n",
              "1  10.000000000  95.000000000   89   45                 None     0  \n",
              "2  11.000000000  96.000000000  135   45  Normal Sinus Rhythm     0  \n",
              "3  32.000000000  91.000000000  102   46                 None     2  \n",
              "4  34.000000000  92.000000000   73   40                 None  None  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1255bec4-3f43-4334-8fbf-c29e7cac9ee1\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>subject_id</th>\n",
              "      <th>stay_id</th>\n",
              "      <th>charttime</th>\n",
              "      <th>temperature</th>\n",
              "      <th>heartrate</th>\n",
              "      <th>resprate</th>\n",
              "      <th>o2sat</th>\n",
              "      <th>sbp</th>\n",
              "      <th>dbp</th>\n",
              "      <th>rhythm</th>\n",
              "      <th>pain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10014967</td>\n",
              "      <td>36889300</td>\n",
              "      <td>2198-09-17 09:01:00</td>\n",
              "      <td>96.400000000</td>\n",
              "      <td>70.000000000</td>\n",
              "      <td>30.000000000</td>\n",
              "      <td>95.000000000</td>\n",
              "      <td>179</td>\n",
              "      <td>103</td>\n",
              "      <td>None</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10015057</td>\n",
              "      <td>34668050</td>\n",
              "      <td>2151-04-15 01:10:00</td>\n",
              "      <td>97.800000000</td>\n",
              "      <td>69.000000000</td>\n",
              "      <td>10.000000000</td>\n",
              "      <td>95.000000000</td>\n",
              "      <td>89</td>\n",
              "      <td>45</td>\n",
              "      <td>None</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10023708</td>\n",
              "      <td>32140682</td>\n",
              "      <td>2144-05-18 09:06:00</td>\n",
              "      <td>97.100000000</td>\n",
              "      <td>63.000000000</td>\n",
              "      <td>11.000000000</td>\n",
              "      <td>96.000000000</td>\n",
              "      <td>135</td>\n",
              "      <td>45</td>\n",
              "      <td>Normal Sinus Rhythm</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10036086</td>\n",
              "      <td>39396729</td>\n",
              "      <td>2206-03-05 23:59:00</td>\n",
              "      <td>None</td>\n",
              "      <td>101.000000000</td>\n",
              "      <td>32.000000000</td>\n",
              "      <td>91.000000000</td>\n",
              "      <td>102</td>\n",
              "      <td>46</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10036086</td>\n",
              "      <td>39396729</td>\n",
              "      <td>2206-03-06 00:00:00</td>\n",
              "      <td>None</td>\n",
              "      <td>102.000000000</td>\n",
              "      <td>34.000000000</td>\n",
              "      <td>92.000000000</td>\n",
              "      <td>73</td>\n",
              "      <td>40</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Investigando a coluna heartrate"
      ],
      "metadata": {
        "id": "9wrKrfrJ3KPs"
      },
      "id": "9wrKrfrJ3KPs"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Distribuição da coluna heartrate"
      ],
      "metadata": {
        "id": "1RMBh1Bx38HN"
      },
      "id": "1RMBh1Bx38HN"
    },
    {
      "cell_type": "code",
      "source": [
        "sinais_vitais['heartrate_num'] = pd.to_numeric(sinais_vitais['heartrate'])\n",
        "sinais_vitais[['heartrate_num']].plot.hist(bins=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "-wZvcq9fdoCK",
        "outputId": "c7704793-f34e-458c-aa3c-d84ebadf48ca"
      },
      "id": "-wZvcq9fdoCK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Análise de outliers da coluna heartrate"
      ],
      "metadata": {
        "id": "1jnHT_fN356O"
      },
      "id": "1jnHT_fN356O"
    },
    {
      "cell_type": "code",
      "source": [
        "sinais_vitais[['heartrate_num']].plot.box()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "RtKW2_Tr35MW",
        "outputId": "c0287a30-49ec-4117-a97a-1724655dffe7"
      },
      "id": "RtKW2_Tr35MW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "taxacardiaca_corte = sinais_vitais.loc[sinais_vitais['heartrate_num'] < 250,'heartrate_num']\n",
        "taxacardiaca_corte.plot.hist(bins=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "3jNSzNmLAWQ4",
        "outputId": "fe0de8e6-7468-479e-c566-d89e851101b6"
      },
      "id": "3jNSzNmLAWQ4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Frequência cardíaca normal por idade:\n",
        "* até 2 anos — 120 a 140 bpm;\n",
        "* 8 até 17 anos — 80 a 100 bpm;\n",
        "* adulto sedentário — 70 a 80 bpm;\n",
        "* adultos que praticam atividades físicas e idosos — 50 a 60 bpm.\n"
      ],
      "metadata": {
        "id": "e05bzsKI4LF0"
      },
      "id": "e05bzsKI4LF0"
    },
    {
      "cell_type": "code",
      "source": [
        "sv_pac = sinais_vitais[['subject_id','heartrate_num']].merge(pacientes[['subject_id','anchor_age']], on='subject_id')\n",
        "sv_pac[sv_pac['heartrate_num']<250].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8GjfAyTuSvxR",
        "outputId": "e3c15c4f-33f8-4f9a-d04d-fb9000d02617"
      },
      "id": "8GjfAyTuSvxR",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   subject_id  heartrate_num  anchor_age\n",
              "0    10014967           70.0          65\n",
              "1    10014967           67.0          65\n",
              "2    10014967           51.0          65\n",
              "3    10014967           53.0          65\n",
              "4    10014967           78.0          65"
            ],
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              "\n",
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              "      <th>subject_id</th>\n",
              "      <th>heartrate_num</th>\n",
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              "      <td>10014967</td>\n",
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              "      <td>65</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10014967</td>\n",
              "      <td>67.0</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10014967</td>\n",
              "      <td>51.0</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10014967</td>\n",
              "      <td>53.0</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10014967</td>\n",
              "      <td>78.0</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          const element = document.querySelector('#df-c1740265-e3bb-4227-9433-fd62c2c386cc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sv_pac[sv_pac['heartrate_num']<250].plot.scatter(x='anchor_age', y='heartrate_num', s=5, figsize=(15,10), c='black', title='Frequência cardíaca por idade')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "id": "g6-ExXSCOVVL",
        "outputId": "5a07633c-1f90-4445-b189-ddfbc9c86ea0"
      },
      "id": "g6-ExXSCOVVL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Frequência cardíaca por idade'}, xlabel='anchor_age', ylabel='heartrate_num'>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Análises a serem implementadas\n",
        "\n",
        "1. Faça um estudo da distribuição e análise de outliers da taxa respiratória, temperatura dos pacientes, pressão arterial (sbp, dbp) e saturação de oxigênio (o2sat). Existem valores estranhos?\n",
        "2. É necessário fazer um cálculo para saber a idade do paciente quando esses sinais vitais foram medidos e portanto os gráficos acima não refletem a realidade da população. Faça o ajuste necessário.\n",
        "3. Existem dados de sinais vitais na base MIMIC-ICU, verifique e investigue em qual tabela se encontra. Se necessário, consulte https://github.com/MIT-LCP/mimic-iv/tree/master/concepts/measurement."
      ],
      "metadata": {
        "id": "mkZlfsQNRxdp"
      },
      "id": "mkZlfsQNRxdp"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Caracterização do paciente"
      ],
      "metadata": {
        "id": "dboL4dqs7ssy"
      },
      "id": "dboL4dqs7ssy"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Análises a serem implementadas\n",
        "\n",
        "##### Investigando o fluxo do paciente numa hospitalização\n"
      ],
      "metadata": {
        "id": "wK-bn6wY8VBe"
      },
      "id": "wK-bn6wY8VBe"
    },
    {
      "cell_type": "code",
      "source": [
        "sql = \"\"\"SELECT t.subject_id, t.hadm_id, t.careunit, t.intime, t.outtime \n",
        "        FROM `physionet-data.mimiciv_hosp.transfers` t\n",
        "        WHERE t.hadm_id = 27882036 and t.subject_id = 10012853;\"\"\"\n",
        "transferencias = pd.read_gbq(sql, projeto)\n",
        "transferencias.reset_index(inplace=True)\n",
        "transferencias = transferencias.loc[0:4,['index','careunit','intime','outtime']]\n",
        "transferencias.loc[transferencias['careunit'] == 'Trauma SICU (TSICU)','index'] = 3\n",
        "df_grafico = pd.concat([transferencias.iloc[:,0:3].rename(columns={'intime':'time'}),transferencias.iloc[:,[0,1,3]].rename(columns={'outtime':'time'})])\n",
        "df_grafico.sort_values(['index','time'], inplace=True)\n",
        "df_grafico.set_index('time',inplace=True)\n",
        "df_grafico.groupby('careunit')['index'].plot(legend=True, xlabel='Período', ylabel='Ala', marker='o', figsize=(15,10), title='Transferências da paciente durante hospitalização')"
      ],
      "metadata": {
        "id": "T2hmD1ti9Z1Z"
      },
      "id": "T2hmD1ti9Z1Z",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![transferencias_paciente_hospitalizado.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "v6HvC7q3-n5C"
      },
      "id": "v6HvC7q3-n5C"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Investigando os sinais vitais do paciente internado no CTI."
      ],
      "metadata": {
        "id": "yCFof9Hf9bdT"
      },
      "id": "yCFof9Hf9bdT"
    },
    {
      "cell_type": "code",
      "source": [
        "sql = \"\"\"\n",
        "select\n",
        "    ce.subject_id\n",
        "  , ce.stay_id\n",
        "  , ce.charttime\n",
        "  , AVG(case when itemid in (220045) and valuenum > 0 and valuenum < 300 then valuenum else null end) as heart_rate\n",
        "  , AVG(case when itemid in (220179,220050) and valuenum > 0 and valuenum < 400 then valuenum else null end) as sbp\n",
        "  , AVG(case when itemid in (220180,220051) and valuenum > 0 and valuenum < 300 then valuenum else null end) as dbp\n",
        "  , AVG(case when itemid in (220052,220181,225312) and valuenum > 0 and valuenum < 300 then valuenum else null end) as mbp\n",
        "  , AVG(case when itemid = 220179 and valuenum > 0 and valuenum < 400 then valuenum else null end) as sbp_ni\n",
        "  , AVG(case when itemid = 220180 and valuenum > 0 and valuenum < 300 then valuenum else null end) as dbp_ni\n",
        "  , AVG(case when itemid = 220181 and valuenum > 0 and valuenum < 300 then valuenum else null end) as mbp_ni\n",
        "  , AVG(case when itemid in (220210,224690) and valuenum > 0 and valuenum < 70 then valuenum else null end) as resp_rate\n",
        "  , ROUND(\n",
        "      AVG(case when itemid in (223761) and valuenum > 70 and valuenum < 120 then (valuenum-32)/1.8 -- converted to degC in valuenum call\n",
        "              when itemid in (223762) and valuenum > 10 and valuenum < 50  then valuenum else null end)\n",
        "    , 2) as temperature\n",
        "  , MAX(CASE WHEN itemid = 224642 THEN value ELSE NULL END) AS temperature_site\n",
        "  , AVG(case when itemid in (220277) and valuenum > 0 and valuenum <= 100 then valuenum else null end) as spo2\n",
        "  , AVG(case when itemid in (225664,220621,226537) and valuenum > 0 then valuenum else null end) as glucose\n",
        "  FROM physionet-data.mimiciv_icu.chartevents ce\n",
        "  where ce.stay_id IS NOT NULL and ce.subject_id = 10012853\n",
        "  and ce.itemid in\n",
        "  (\n",
        "    220045, -- Heart Rate\n",
        "    225309, -- ART BP Systolic\n",
        "    225310, -- ART BP Diastolic\n",
        "    225312, -- ART BP Mean\n",
        "    220050, -- Arterial Blood Pressure systolic\n",
        "    220051, -- Arterial Blood Pressure diastolic\n",
        "    220052, -- Arterial Blood Pressure mean\n",
        "    220179, -- Non Invasive Blood Pressure systolic\n",
        "    220180, -- Non Invasive Blood Pressure diastolic\n",
        "    220181, -- Non Invasive Blood Pressure mean\n",
        "    220210, -- Respiratory Rate\n",
        "    224690, -- Respiratory Rate (Total)\n",
        "    220277, -- SPO2, peripheral\n",
        "    -- GLUCOSE, both lab and fingerstick\n",
        "    225664, -- Glucose finger stick\n",
        "    220621, -- Glucose (serum)\n",
        "    226537, -- Glucose (whole blood)\n",
        "    -- TEMPERATURE\n",
        "    223762, -- \"Temperature Celsius\"\n",
        "    223761,  -- \"Temperature Fahrenheit\"\n",
        "    224642 -- Temperature Site\n",
        "    -- 226329 -- Blood Temperature CCO (C)\n",
        ")\n",
        "group by ce.subject_id, ce.stay_id, ce.charttime\n",
        ";\"\"\"\n",
        "\n",
        "sinal_vital_paciente = pd.read_gbq(sql, projeto) "
      ],
      "metadata": {
        "id": "hBkE8Un5AZUg"
      },
      "id": "hBkE8Un5AZUg",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sinal_vital_paciente.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6s5PHrcvCSkr",
        "outputId": "4643396d-0b9f-43af-8204-efe769b82c90"
      },
      "id": "6s5PHrcvCSkr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   subject_id   stay_id           charttime  heart_rate    sbp   dbp   mbp  \\\n",
              "0    10012853  31338022 2176-11-27 07:00:00        61.0    NaN   NaN   NaN   \n",
              "1    10012853  31338022 2176-11-28 16:01:00         NaN   75.0  48.0  53.0   \n",
              "2    10012853  31338022 2176-11-27 20:00:00        68.0    NaN   NaN   NaN   \n",
              "3    10012853  31338022 2176-11-27 18:01:00         NaN  132.0  45.0  67.0   \n",
              "4    10012853  31338022 2176-11-26 14:01:00         NaN  127.0  37.0  61.0   \n",
              "\n",
              "   sbp_ni  dbp_ni  mbp_ni  resp_rate  temperature temperature_site  spo2  \\\n",
              "0     NaN     NaN     NaN       22.0          NaN             None  92.0   \n",
              "1    75.0    48.0    53.0        NaN          NaN             None   NaN   \n",
              "2     NaN     NaN     NaN       23.0        37.11             Oral  96.0   \n",
              "3   132.0    45.0    67.0        NaN          NaN             None   NaN   \n",
              "4   127.0    37.0    61.0        NaN          NaN             None   NaN   \n",
              "\n",
              "   glucose  \n",
              "0     83.0  \n",
              "1      NaN  \n",
              "2    113.0  \n",
              "3      NaN  \n",
              "4      NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-afabf850-f74a-4cad-826a-7e95611ac97d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>subject_id</th>\n",
              "      <th>stay_id</th>\n",
              "      <th>charttime</th>\n",
              "      <th>heart_rate</th>\n",
              "      <th>sbp</th>\n",
              "      <th>dbp</th>\n",
              "      <th>mbp</th>\n",
              "      <th>sbp_ni</th>\n",
              "      <th>dbp_ni</th>\n",
              "      <th>mbp_ni</th>\n",
              "      <th>resp_rate</th>\n",
              "      <th>temperature</th>\n",
              "      <th>temperature_site</th>\n",
              "      <th>spo2</th>\n",
              "      <th>glucose</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10012853</td>\n",
              "      <td>31338022</td>\n",
              "      <td>2176-11-27 07:00:00</td>\n",
              "      <td>61.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>92.0</td>\n",
              "      <td>83.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10012853</td>\n",
              "      <td>31338022</td>\n",
              "      <td>2176-11-28 16:01:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10012853</td>\n",
              "      <td>31338022</td>\n",
              "      <td>2176-11-27 20:00:00</td>\n",
              "      <td>68.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>37.11</td>\n",
              "      <td>Oral</td>\n",
              "      <td>96.0</td>\n",
              "      <td>113.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10012853</td>\n",
              "      <td>31338022</td>\n",
              "      <td>2176-11-27 18:01:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>132.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10012853</td>\n",
              "      <td>31338022</td>\n",
              "      <td>2176-11-26 14:01:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>127.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>61.0</td>\n",
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              "      <td>37.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "sinal_vital_paciente.shape"
      ],
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        {
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          "data": {
            "text/plain": [
              "(181, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sinal_vital_paciente['stay_id'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0PzAxMBBuqs",
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              "<IntegerArray>\n",
              "[31338022]\n",
              "Length: 1, dtype: Int64"
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    {
      "cell_type": "code",
      "source": [
        "df_grafico = sinal_vital_paciente.loc[:,['charttime', 'heart_rate', 'sbp', 'dbp','spo2']]\n",
        "df_grafico.rename(columns = {'charttime':'hora'}, inplace = True)\n",
        "df_grafico.sort_values(by='hora', inplace=True)\n",
        "df_grafico.set_index('hora', inplace=True)\n",
        "df_grafico.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "hRwwz8UWCYpH",
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      "id": "hRwwz8UWCYpH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     heart_rate    sbp   dbp  spo2\n",
              "hora                                              \n",
              "2176-11-26 02:34:00        77.0    NaN   NaN  94.0\n",
              "2176-11-26 02:35:00         NaN  164.0  63.0   NaN\n",
              "2176-11-26 02:36:00        73.0    NaN   NaN  96.0\n",
              "2176-11-26 03:00:00        70.0    NaN   NaN  93.0\n",
              "2176-11-26 04:00:00        66.0    NaN   NaN  90.0"
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              "                                                     [key], {});\n",
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              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "df_grafico.plot(kind='line', legend=True, xlabel='Período', ylabel='Valor', marker='.', figsize=(15,10), title='Sinais vitais da paciente durante a hospitalização')"
      ],
      "metadata": {
        "colab": {
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          "height": 829
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        "id": "rNQD5MZbD0Lr",
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      "id": "rNQD5MZbD0Lr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Sinais vitais da paciente durante a hospitalização'}, xlabel='Período', ylabel='Valor'>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Obs.: o pacote matplotlib apenas desenha uma linha entre dois pontos consecutivos com valores válidos e deixa em branco nos valores NaN.\n",
        "* Para desenhar apenas o gráfico podemos preencher o dataframe utilizando a função *fillna*. Confira abaixo o resultado e teste outros valores para o parâmetro '*method*'. "
      ],
      "metadata": {
        "id": "p_Fc2earHcSr"
      },
      "id": "p_Fc2earHcSr"
    },
    {
      "cell_type": "code",
      "source": [
        "df_grafico.fillna(method='bfill').plot(kind='line', legend=True, xlabel='Período', ylabel='Valor', marker='.', figsize=(15,10), title='Sinais vitais da paciente durante a hospitalização')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "FuNFGX7_G_hc",
        "outputId": "ffb41e21-4b06-4ddf-80ff-5567b20d3a67"
      },
      "id": "FuNFGX7_G_hc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Sinais vitais da paciente durante a hospitalização'}, xlabel='Período', ylabel='Valor'>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Análises a serem implementadas\n",
        "* Teste outras abordagens para tratar esse problemas, como uso da média, remoção de registros, etc. No fim do semestre, voltaremos a esse assunto tratando outros métodos. Esse passo será importante para a próxima tarefa, onde você conseguirá testar a diferença de diferentes formas de imputação. "
      ],
      "metadata": {
        "id": "IPw7t1l3LjmV"
      },
      "id": "IPw7t1l3LjmV"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Previsão de Mortalidade\n",
        "Até o momento, fizemos análises para entender melhor os dados da MIMIC-IV. Agora, criaremos um modelo simples para prever se o paciente irá sobreviver ou não depois de hospitalizado. Para isso, utilizaremos regressão logística.\n",
        "- A tarefa é prever se o paciente vai sobreviver ou não utilizando um método de classificação.\n",
        "- A princípio, iremos utilizar dados demográficos do paciente: idade, estado civil, raça, idioma. Iniciaremos com um método de regressão logística."
      ],
      "metadata": {
        "id": "66-m2xVULnHc"
      },
      "id": "66-m2xVULnHc"
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.impute import SimpleImputer\n",
        "# from sklearn.pipeline import Pipeline\n",
        "# from sklearn.pipeline import make_pipeline\n",
        "# from sklearn.preprocessing import OrdinalEncoder\n",
        "# from sklearn.compose import ColumnTransformer\n",
        "# from sklearn.feature_selection import SelectPercentile\n",
        "# from sklearn.feature_selection import chi2\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.compose import make_column_transformer"
      ],
      "metadata": {
        "id": "wz7oom3vLrNs"
      },
      "id": "wz7oom3vLrNs",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Selecionar dados"
      ],
      "metadata": {
        "id": "JvJc6uTIOI01"
      },
      "id": "JvJc6uTIOI01"
    },
    {
      "cell_type": "code",
      "source": [
        "demografico = get_tabela(projeto, dataset_hosp, 'patients', 'subject_id, anchor_age')\n",
        "demog_atendimento = get_tabela(projeto, dataset_hosp, 'admissions', 'subject_id, hadm_id, marital_status, race, language, hospital_expire_flag')\n",
        "demografico = demografico.merge(demog_atendimento, on='subject_id')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dvcvy33wNx36",
        "outputId": "36f41885-7166-43ab-f832-de55f6bd2ba0"
      },
      "id": "Dvcvy33wNx36",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SELECT subject_id, anchor_age FROM `physionet-data.mimiciv_hosp.patients`;\n",
            "SELECT subject_id, hadm_id, marital_status, race, language, hospital_expire_flag FROM `physionet-data.mimiciv_hosp.admissions`;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "demografico.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8rSenySnOAWt",
        "outputId": "db4eb62e-6420-4aad-c4d7-317f95faf923"
      },
      "id": "8rSenySnOAWt",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   subject_id  anchor_age   hadm_id marital_status  \\\n",
              "0    10686175          18  27744664         SINGLE   \n",
              "1    11289691          18  23182566         SINGLE   \n",
              "2    11289691          18  25537045         SINGLE   \n",
              "3    11595073          18  26034097         SINGLE   \n",
              "4    11776346          18  29730252         SINGLE   \n",
              "\n",
              "                            race language  hospital_expire_flag  \n",
              "0  AMERICAN INDIAN/ALASKA NATIVE  ENGLISH                     0  \n",
              "1         BLACK/AFRICAN AMERICAN  ENGLISH                     0  \n",
              "2         BLACK/AFRICAN AMERICAN  ENGLISH                     0  \n",
              "3                          WHITE  ENGLISH                     0  \n",
              "4           ASIAN - ASIAN INDIAN  ENGLISH                     0  "
            ],
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Análises a serem implementadas\n",
        "* Aqui a idade deve ser calculada da mesma forma que a sugestão 2 da seção *Investigando a coluna heartrate*! "
      ],
      "metadata": {
        "id": "2TTULMDAQx3C"
      },
      "id": "2TTULMDAQx3C"
    },
    {
      "cell_type": "code",
      "source": [
        "# recalcular a idade dos pacientes de acordo com a data da hospitalização."
      ],
      "metadata": {
        "id": "4Z-RJ-zRRYyf"
      },
      "id": "4Z-RJ-zRRYyf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "demografico.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eg6qZXp5OVg3",
        "outputId": "ebcf26a4-da4f-4998-b5ca-083be5ee767c"
      },
      "id": "eg6qZXp5OVg3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(431231, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "demografico['subject_id'].unique().size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aIfTG0wfOXb0",
        "outputId": "c9d834fc-9a69-4336-d649-fbf9009ddde8"
      },
      "id": "aIfTG0wfOXb0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "180733"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Analisar classes"
      ],
      "metadata": {
        "id": "k4p-w7yVPZV_"
      },
      "id": "k4p-w7yVPZV_"
    },
    {
      "cell_type": "code",
      "source": [
        "demografico[['subject_id','hospital_expire_flag']].drop_duplicates()['hospital_expire_flag'].plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "mXAA12ycOgzU",
        "outputId": "fb516f6e-1a64-4019-9ac8-5ad82d54356d"
      },
      "id": "mXAA12ycOgzU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Análises a serem implementadas\n",
        "- Note que há um desbalanceamento de classes. Como tratar?"
      ],
      "metadata": {
        "id": "Kj1DQTO_PNdd"
      },
      "id": "Kj1DQTO_PNdd"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Analisar dados faltantes"
      ],
      "metadata": {
        "id": "BjedbwXGRw6L"
      },
      "id": "BjedbwXGRw6L"
    },
    {
      "cell_type": "code",
      "source": [
        "demografico.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50KsBqxmSqHm",
        "outputId": "0c9c36a6-f5ca-49ed-b90a-49140c667ecd"
      },
      "id": "50KsBqxmSqHm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['subject_id', 'anchor_age', 'hadm_id', 'marital_status', 'race',\n",
              "       'language', 'hospital_expire_flag'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "demografico.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNJqa4vbPM-O",
        "outputId": "8db8a5f1-dd82-42cb-d94c-62403589faef"
      },
      "id": "QNJqa4vbPM-O",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 431231 entries, 0 to 431230\n",
            "Data columns (total 7 columns):\n",
            " #   Column                Non-Null Count   Dtype \n",
            "---  ------                --------------   ----- \n",
            " 0   subject_id            431231 non-null  Int64 \n",
            " 1   anchor_age            431231 non-null  Int64 \n",
            " 2   hadm_id               431231 non-null  Int64 \n",
            " 3   marital_status        421998 non-null  object\n",
            " 4   race                  431231 non-null  object\n",
            " 5   language              431231 non-null  object\n",
            " 6   hospital_expire_flag  431231 non-null  Int64 \n",
            "dtypes: Int64(4), object(3)\n",
            "memory usage: 28.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4. Particionar conjunto de dados (treino/teste)"
      ],
      "metadata": {
        "id": "hEe6uGFCSU5f"
      },
      "id": "hEe6uGFCSU5f"
    },
    {
      "cell_type": "code",
      "source": [
        "df_modelo = demografico.loc[:,['anchor_age', 'marital_status', 'race', 'language', 'hospital_expire_flag']].drop_duplicates()"
      ],
      "metadata": {
        "id": "oE9yRogvYEhV"
      },
      "id": "oE9yRogvYEhV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_modelo.loc[:,['anchor_age', 'marital_status', 'race', 'language']]\n",
        "y = df_modelo.loc[:,'hospital_expire_flag']\n",
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X,y,\n",
        "                                   random_state=104, \n",
        "                                   test_size=0.10, \n",
        "                                   shuffle=True)"
      ],
      "metadata": {
        "id": "TXpibIpCOnvq"
      },
      "id": "TXpibIpCOnvq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Análises a serem implementadas\n",
        "- Note que como o particionamento foi realizado, um mesmo paciente pode estar em ambos conjuntos, de treino e teste. Por que é um problema? E como resolver?\n",
        "- Além disso, o mesmo paciente pode ter várias hospitalizações mas apenas na última ele faleceu. Isso é um problema? Explique e sugira uma solução se for o caso."
      ],
      "metadata": {
        "id": "zl2AdYjuVddX"
      },
      "id": "zl2AdYjuVddX"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 5. Criar modelo"
      ],
      "metadata": {
        "id": "QmS7g7x2TTnk"
      },
      "id": "QmS7g7x2TTnk"
    },
    {
      "cell_type": "code",
      "source": [
        "#Ao rodar o comando fit no fim dessa célula, o Google colab gera automaticamente o pipeline seguido\n",
        "#Esse fluxo não aparece em outros ambientes.\n",
        "variaveis_numericas = ['anchor_age']\n",
        "variaveis_categoricas = ['marital_status', 'race', 'language']\n",
        "\n",
        "preprocessamento =  make_column_transformer(\n",
        "    (OneHotEncoder(), variaveis_categoricas),\n",
        ")\n",
        "\n",
        "modelo1 = make_pipeline(\n",
        "    preprocessamento,\n",
        "    LogisticRegression(solver = \"liblinear\", class_weight=\"balanced\"),\n",
        ")\n",
        "\n",
        "modelo2 = make_pipeline(\n",
        "    preprocessamento,\n",
        "    LogisticRegression(solver = \"liblinear\"),\n",
        ")\n",
        "\n",
        "\n",
        "modelo1.fit(X_treino, y_treino)\n",
        "modelo2.fit(X_treino, y_treino)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "38lfGREiSTeU",
        "outputId": "16a9b8ed-7d4c-4d6b-c1d9-e3daa3866761"
      },
      "id": "38lfGREiSTeU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(transformers=[('onehotencoder',\n",
              "                                                  OneHotEncoder(),\n",
              "                                                  ['marital_status', 'race',\n",
              "                                                   'language'])])),\n",
              "                ('logisticregression', LogisticRegression(solver='liblinear'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(),\n",
              "                                                  [&#x27;marital_status&#x27;, &#x27;race&#x27;,\n",
              "                                                   &#x27;language&#x27;])])),\n",
              "                (&#x27;logisticregression&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(),\n",
              "                                                  [&#x27;marital_status&#x27;, &#x27;race&#x27;,\n",
              "                                                   &#x27;language&#x27;])])),\n",
              "                (&#x27;logisticregression&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
              "                                 [&#x27;marital_status&#x27;, &#x27;race&#x27;, &#x27;language&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;marital_status&#x27;, &#x27;race&#x27;, &#x27;language&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####**Análises**\n",
        "\n",
        "* Qual a diferença entre modelo1 e modelo2?"
      ],
      "metadata": {
        "id": "-03X-Zn8XNzm"
      },
      "id": "-03X-Zn8XNzm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 6. Rodar e analisar "
      ],
      "metadata": {
        "id": "AT32oHELUbKo"
      },
      "id": "AT32oHELUbKo"
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred1 = modelo1.predict(X_teste)\n",
        "print(\"Score do modelo1: %.3f\" % modelo1.score(X_teste, y_teste))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bj_tQMr8UaG0",
        "outputId": "b10a4dd2-bc95-45bc-a502-3caaeb9000be"
      },
      "id": "Bj_tQMr8UaG0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score do modelo1: 0.661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred2 = modelo2.predict(X_teste)\n",
        "print(\"Score do modelo2: %.3f\" % modelo2.score(X_teste, y_teste))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ijiZmhCU56E",
        "outputId": "3dfe779e-0a61-4dfd-d618-4709e9421603"
      },
      "id": "8ijiZmhCU56E",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score do modelo1: 0.845\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####**Análises**\n",
        "\n",
        "* Explique a diferença de resultados entre modelo1 e modelo2. \n",
        "* Que outros atributos poderiam ser utilizados para essa tarefa, visando melhorar o resultado do modelo.\n",
        "* Se desejar, crie um novo modelo com novos atributos ou formas de tratar dados faltantes e desbalanceamento de classses."
      ],
      "metadata": {
        "id": "2d48GUJtXrRA"
      },
      "id": "2d48GUJtXrRA"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P-sHIwctVJc0"
      },
      "id": "P-sHIwctVJc0",
      "execution_count": null,
      "outputs": []
    }
  ],
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      "language": "python",
      "name": "python3"
    },
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.2"
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      "toc_visible": true,
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