20221119_데이터_핸들링.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ZzangJay/team_project/blob/main/20221119_%EB%8D%B0%EC%9D%B4%ED%84%B0_%ED%95%B8%EB%93%A4%EB%A7%81.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xCBwZEG88iTZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36a5a23d-072c-4e21-ff2a-e6d3b1a32201"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  import sys\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "#import tensorflow as tf \n",
        "import os\n",
        "import sys\n",
        "from datetime import datetime, timedelta\n",
        "from tqdm.autonotebook import tqdm\n",
        "import csv\n",
        "import re\n",
        "import gzip\n",
        "import shutil"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKfa-fAw8qIn",
        "outputId": "b73e4684-66ce-416d-a498-efb2fd943dca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['CHANGELOG.txt',\n",
              " 'LICENSE.txt',\n",
              " 'SHA256SUMS.txt',\n",
              " 'index.html',\n",
              " 'icu',\n",
              " 'hosp',\n",
              " 'drop_2_chartevents.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from google.colab import drive  # for colab\n",
        "drive.mount('/content/gdrive/')  # for colab\n",
        "cloud_directory = '/content/gdrive/MyDrive/mimiciv_unzip/'\n",
        "os.listdir(cloud_directory) # for colab, check cloud directory mount"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gicdKWPb8xfP"
      },
      "outputs": [],
      "source": [
        "admission = pd.read_csv('/content/gdrive/MyDrive/mimiciv_unzip/hosp/admissions.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "patients = pd.read_csv('/content/gdrive/MyDrive/mimiciv_unzip/hosp/patients.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "diagnoses_icd = pd.read_csv('/content/gdrive/MyDrive/mimiciv_unzip/hosp/diagnoses_icd.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "icustays = pd.read_csv('/content/gdrive/MyDrive/mimiciv_unzip/icu/icustays.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "sepsis = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/sepsis3.csv', low_memory=False, dtype='str', keep_default_na=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M4GF5YNSEXLE"
      },
      "outputs": [],
      "source": [
        "admission = pd.merge(left = admission, right = sepsis[['subject_id','sepsis3']], how='inner', left_on='subject_id', right_on='subject_id')\n",
        "patients = pd.merge(left = patients, right = sepsis[['subject_id','sepsis3']], how='inner', left_on='subject_id', right_on='subject_id')\n",
        "diagnoses_icd = pd.merge(left = diagnoses_icd, right = sepsis[['subject_id','sepsis3']], how='inner', left_on='subject_id', right_on='subject_id')\n",
        "sepsis = pd.merge(left = sepsis, right = icustays[['hadm_id','stay_id']], how='left', left_on='stay_id', right_on='stay_id')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "extra_features = pd.read_csv('/content/gdrive/MyDrive/mimiciv preprocessing_js/extra_features2.csv', low_memory=False, keep_default_na=False)\n",
        "sepsis2 = pd.merge(left = sepsis, right = extra_features['hadm_id'].astype(str), how='inner', left_on='hadm_id', right_on='hadm_id')"
      ],
      "metadata": {
        "id": "KacGGcEdva5i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id_cepha = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_cepha.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_glyco = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_glyco.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_peni = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_peni.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_fluoro = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_fluoro.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_macro = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_macro.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_cepha['LABEL'] = \"cepha\"\n",
        "id_glyco['LABEL'] = \"glyco\"\n",
        "id_peni['LABEL'] = \"peni\"\n",
        "id_fluoro['LABEL'] = \"fluoro\"\n",
        "id_macro['LABEL'] = \"macro\"\n",
        "\n",
        "id = pd.concat([id_cepha,id_glyco,id_peni,id_fluoro,id_macro])\n",
        "\n",
        "id.groupby(['LABEL']).count()"
      ],
      "metadata": {
        "id": "_a9_qw2lRWCk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Total_HADMIDS = set(sepsis2['hadm_id'])\n",
        "len(Total_HADMIDS)\n",
        "# Total_HADMIDS = set(sepsis['hadm_id'])\n",
        "# len(Total_HADMIDS)\n",
        "#32812"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LH_5n3H-Lc1R",
        "outputId": "43e7c9d1-2671-497f-9820-049efda42208"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32365"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xD5AA-mHrl4B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d4f9f65-a1e1-42ca-c50b-daca54daf320"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22\n"
          ]
        }
      ],
      "source": [
        "# feature_keys = ['FiO2', 'HR', 'PaO2', 'SBP', 'Temperature','Creatinine','Glucose', 'Hb', 'Lactate', 'Plt','Sodium' ,'WBC']\n",
        "# feature_keys = ['FiO2', 'HR', 'PaO2', 'SBP', 'Temperature','Creatinine','Glucose', 'Hb', 'Lactate', 'Plt','Sodium' ,'WBC', 'DBP', 'MAP', 'RESP', 'Chloride', 'Hct', 'PCO2', 'Potassium', 'PH', 'Anion Gap', 'Bilirubin']\n",
        "feature_keys = ['FiO2', 'HR', 'PaO2', 'SBP', 'Temperature','Creatinine','Glucose', 'Hb', 'Lactate', 'Plt','Sodium' ,'WBC', 'DBP', 'MAP', 'RESP', 'Chloride', 'Hct', 'PCO2', 'Potassium', 'PH', 'Anion Gap', 'Bilirubin']\n",
        "print(len(feature_keys))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yt6qYxMBwAtn"
      },
      "outputs": [],
      "source": [
        "EXTRACT_FEATURE_CODES_VS = dict({\n",
        "    '226754': 'FiO2', '223835': 'FiO2', '227010': 'FiO2', '229280':'FiO2', '229841': 'FiO2',\n",
        "    '220045': 'HR', '220046': 'HR', '226764': 'HR', '227018': 'HR',\n",
        "    '220224': 'PaO2', '227039': 'PaO2', '227516': 'PaO2',\n",
        "    '220050': 'SBP' ,'220179': 'SBP', '224167': 'SBP', '227243': 'SBP', '228152': 'SBP',\n",
        "    '223762': 'Temperature', '226329': 'Temperature', '227632': 'Temperature', '227634': 'Temperature',\n",
        "    '223761': 'TEMP_F',\n",
        "    '225310': 'DBP', '220051': 'DBP', '220180': 'DBP',\n",
        "    '225312': 'MAP', '220052': 'MAP', '220181': 'MAP',\n",
        "    '220210': 'RESP', '224690': 'RESP'\n",
        "    })\n",
        "EXTRACT_FEATURE_CODES_LAB = dict({\n",
        "    '220615': 'Creatinine', '229761': 'Creatinine',\n",
        "    '52024': 'Creatinine', '50912': 'Creatinine', '52546': 'Creatinine', '51977': 'Creatinine',  '51081': 'Creatinine',\n",
        "    '220621': 'Glucose', '225664': 'Glucose', '226537': 'Glucose',\n",
        "    '50809': 'Glucose', '52027': 'Glucose', '50931': 'Glucose', '52569': 'Glucose', '51981': 'Glucose', '51478': 'Glucose',\n",
        "    '220228': 'Hb',\n",
        "    '50811': 'Hb', '50855': 'Hb', '51640': 'Hb', '51645': 'Hb', '51222': 'Hb', '52157': 'Hb',\n",
        "    '225668': 'Lactate',\n",
        "    '50813': 'Lactate', '52442': 'Lactate',\n",
        "    '227457': 'Plt',\n",
        "    '51704': 'Plt', '51265': 'Plt',\n",
        "    '220645': 'Sodium', '226534': 'Sodium',\n",
        "    '50824': 'Sodium', '52455': 'Sodium', '50983': 'Sodium', '52623': 'Sodium',\n",
        "    '220546': 'WBC',\n",
        "    '51755': 'WBC', '51756': 'WBC', '51301': 'WBC',\n",
        "    '50805': 'Chloride', '50902': 'Chloride',\n",
        "    '50810': 'Hct',\n",
        "    '50810': 'PCO2',\n",
        "    '50822': 'Potassium', '50971': 'Potassium',\n",
        "    '50820': 'PH',\n",
        "    '50868': 'Anion Gap',\n",
        "    '50885': 'Bilirubin'\n",
        "    }) \n",
        "EXTRACT_FEATURE_CODES_NORMAL = dict({\n",
        "        'FiO2': [10, 100],\n",
        "        'HR': [30, 200],\n",
        "        'PaO2': [50, 200],\n",
        "        'SBP': [40, 250],\n",
        "        'Temperature': [30, 45],\n",
        "        'Creatinine': [0.2, 100],\n",
        "        'Glucose': [30, 1000],\n",
        "        'Hb': [5, 20],\n",
        "        'Lactate': [0.5, 2.5],\n",
        "        'Plt': [10, 500],\n",
        "        'Sodium': [90, 170],\n",
        "        'WBC': [1, 30],\n",
        "        'DBP':[30,200],\n",
        "        'MAP':[33,216],\n",
        "        'RESP':[0,35],\n",
        "        'Chloride':[70,130],\n",
        "        'Hct':[10,50],\n",
        "        'PCO2':[5,100],\n",
        "        'Potassium':[1.0,10.0],\n",
        "        'PH':[5.5,8.0],\n",
        "        'Anion Gap':[5,50],\n",
        "        'Bilirubin':[0.1,10]\n",
        "})\n",
        "NORMAL_VALUE = dict({\n",
        "        'FiO2': 40,\n",
        "        'HR': 75, #(50+100)/2\n",
        "        'PaO2': 87.5, #(75+100)/2\n",
        "        'SBP': 115, #(90+140)/2 = 115\n",
        "        'Temperature': 36.65, #(36.1+37.2)/2\n",
        "        'Creatinine': 1.0, #(0.7+1.0)/2\n",
        "        'Glucose': 100, #(60+140)/2\n",
        "        'Hb': 15.0, #(13.8+17.2)/2\n",
        "        'Lactate': 1.35, #(0.5+2.2)/2 = 1.35\n",
        "        'Plt': 270, #(140+400)/2\n",
        "        'Sodium': 140, #(135+145)/2\n",
        "        'WBC': 7.5, #(5+10)/2\n",
        "        'DBP': 90, #(80+100)/2 \n",
        "        'MAP': 98, # (115 + 2*90) / \n",
        "        'RESP': 13, \n",
        "        'Chloride': 102, #(96+108) / 2\n",
        "        'Hct': 34.5, #(34+45)/2\n",
        "        'PCO2': 40, #(35+45)/2\n",
        "        'Potassium': 4.5, #(3.5+5.4)/2\n",
        "        'PH': 7.4, #(7.35 + 7.45) / 2\n",
        "        'Anion Gap': 14, #8+20/2\n",
        "        'Bilirubin': 0.8 #0+1.5/2\n",
        "})\n",
        "\n",
        "# EXTRACT_FEATURE_CODES_anti = ('225798','225837','225838','225840','225842','225843','225844','225845','225847','225848','225850','225851','225853','225855','225857','225859','225860','225862','225863','225865','225866','225868','225869','225871','225873','225875','225876','225877','225879','225881','225882','225883','225884','225885','225886','225888','225889','225890','225892','225893','225895','225896','225897','225898','225899','225900','225902','225903','225905','227691','228003','229059','229061','229064','229587')\n",
        "# EXTRACT_FEATURE_CODES_ANTI_2 = dict({\n",
        "#    '225850': 'Cephalosporin', '225851': 'Cephalosporin', '225853': 'Cephalosporin', '225855': 'Cephalosporin', '229587':'Cephalosporin',\n",
        "#    '225798': 'Glycopetide',\n",
        "#    '225842': 'Penicillin', '225843': 'Penicillin', '225888': 'Penicillin', '225889': 'Penicillin', '225890': 'Penicillin', '225892': 'Penicillin', '225893': 'Penicillin',\n",
        "#    '225859': 'Quninolone', '225859': 'Quninolone', '225879': 'Quninolone', '225886': 'Quninolone',\n",
        "#    '225845': 'Macrolide', '225865': 'Macrolide', '225866': 'Macrolide'\n",
        "# })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZwMLOPrMI-6d"
      },
      "outputs": [],
      "source": [
        "## Define some util functions\n",
        "NOT_CONVERTED = 'NOT_CONVERTED'\n",
        "## 문자로 기록된 날짜 값들을 파이썬의 datetime 데이터로 바꿔주는 함수 작성\n",
        "def str2datetime(s):\n",
        "    def _convert(_s, _dformat):\n",
        "        try:\n",
        "            converted_dt = datetime.strptime(_s, _dformat)\n",
        "        except Exception:\n",
        "            return NOT_CONVERTED\n",
        "\n",
        "        return converted_dt\n",
        "\n",
        "    if isinstance(s, datetime):\n",
        "        return s\n",
        "\n",
        "    dformats = [\n",
        "        '%Y-%m-%d %p %I:%M:%S',\n",
        "        '%Y-%m-%d %H:%M:%S.%f',\n",
        "        '%Y-%m-%d %H:%M:%S',\n",
        "        '%Y-%m-%d %H:%M',\n",
        "        '%y-%m-%d %H:%M',\n",
        "        '%Y-%m-%d',\n",
        "        '%Y%m%d',\n",
        "        '%Y-%m-%d +%H:%M',\n",
        "        '%Y%m%d %H%M',\n",
        "        '%Y%m%d%H%M%S',\n",
        "        ]\n",
        "\n",
        "    s = s.split('.')[0]\n",
        "    s = s.replace('오전', 'am').replace('오후', 'pm')\n",
        "    for dformat in dformats:\n",
        "        dt = _convert(s, dformat)\n",
        "        if dt != NOT_CONVERTED:\n",
        "            return dt\n",
        "\n",
        "    return NOT_CONVERTED"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l4eGWeCbGRQR"
      },
      "outputs": [],
      "source": [
        "def extract_features():\n",
        "  features_hadm_id = dict()\n",
        "  chartevents = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_unzip/icu/chartevents.csv', 'r', encoding='utf-8'))\n",
        "  labevents = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_unzip/hosp/labevents.csv', 'r', encoding='utf-8'))\n",
        "  for row in tqdm(chartevents, desc='Extract_VS', total=329822285, ncols=700):\n",
        "    if row['hadm_id'] in Total_HADMIDS:\n",
        "      hadm_id = row['hadm_id']\n",
        "      features_hadm_id[hadm_id] = features_hadm_id.get(hadm_id, dict()) #get 없으면 2번째로 return\n",
        "\n",
        "      item_id = row['itemid']\n",
        "      if item_id not in EXTRACT_FEATURE_CODES_VS:\n",
        "        continue\n",
        "      item_name = EXTRACT_FEATURE_CODES_VS[item_id]\n",
        "      try:\n",
        "        #정규식을 이용해서 기록된 값에서 숫자 형태의 값만 가져오기\n",
        "        #소수점 둘째자리 까지만 값을 사용하기\n",
        "        item_value = round(float(re.findall(\"\\d+\\.\\d+|\\d+\", row['valuenum'])[0]), 2)\n",
        "      except Exception:\n",
        "        continue\n",
        "      \n",
        "      item_time = str2datetime(row['charttime'])\n",
        "      if item_time == NOT_CONVERTED:\n",
        "        continue\n",
        "      #GCP total score도 시간대에 총합으로 사용함\n",
        "      if item_name == 'TEMP_F':\n",
        "        item_value = round((item_value - 32.0) / 1.8, 2)\n",
        "        item_name = 'TEMP'\n",
        "\n",
        "    #   if item_value > FEATURE_NORMAL_RANGE[item_name][1]:\n",
        "    #     item_value = FEATURE_NORMAL_RANGE[item_name][1]\n",
        "    #   elif item_value < FEATURE_NORMAL_RANGE[item_name][0]:\n",
        "    #     item_value = FEATURE_NORMAL_RANGE[item_name][0]\n",
        "\n",
        "      features_hadm_id[hadm_id][item_name] = features_hadm_id[hadm_id].get(item_name, list()) #같은 환자의 여러개 feature가 있으면 list로 반환하도록\n",
        "      features_hadm_id[hadm_id][item_name].append((item_time, item_value))\n",
        "      \n",
        "    else:\n",
        "      continue\n",
        "\n",
        "  for row in tqdm(labevents, desc='Extract_LAB', total=124342638, ncols=700):\n",
        "    if row['hadm_id'] in Total_HADMIDS:\n",
        "      hadm_id = row['hadm_id']\n",
        "      features_hadm_id[hadm_id] = features_hadm_id.get(hadm_id, dict())\n",
        "\n",
        "      item_id = row['itemid']\n",
        "      if item_id not in EXTRACT_FEATURE_CODES_LAB:\n",
        "        continue\n",
        "      item_name = EXTRACT_FEATURE_CODES_LAB[item_id]\n",
        "      try:\n",
        "        #정규식을 이용해서 기록된 값에서 숫자 형태의 값만 가져오기\n",
        "        #소수점 둘째자리 까지만 값을 사용하기\n",
        "        item_value = round(float(re.findall(\"\\d+\\.\\d+|\\d+\", row['valuenum'])[0]), 2)\n",
        "      except Exception:\n",
        "        continue\n",
        "      \n",
        "      item_time = str2datetime(row['charttime'])\n",
        "      if item_time == NOT_CONVERTED:\n",
        "        continue\n",
        "\n",
        "      #if item_value > FEATURE_NORMAL_RANGE[item_name][1]:\n",
        "      #  item_value = FEATURE_NORMAL_RANGE[item_name][1]\n",
        "      #elif item_value < FEATURE_NORMAL_RANGE[item_name][0]:\n",
        "      #  item_value = FEATURE_NORMAL_RANGE[item_name][0]\n",
        "        \n",
        "      features_hadm_id[hadm_id][item_name] = features_hadm_id[hadm_id].get(item_name, list())\n",
        "      features_hadm_id[hadm_id][item_name].append((item_time, item_value))\n",
        "      \n",
        "    else:\n",
        "      continue\n",
        "      \n",
        "  return features_hadm_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8IuFnUqELI10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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            "9bcb686b14be44fa868b9aa43db24728",
            "1c07e0026a444b0494318f8326407236",
            "2323199ee9cd436f864d9e7dae496fac",
            "f1ef66e221784107919a2497955b2dfd",
            "05670e601663467e9409f3b73b421173",
            "dc7cbe7df4704d4a99f2d44dc4175fb5",
            "35fd672ed29f42f1809318b67f12edd3",
            "99881b15014c40ddaa2c6faa339eb396",
            "624b2b7b71cf4fa68fc0d3731c812787",
            "8d30ef6d0bef4ee0a935e85e01eaf9f0",
            "722aa58594a0423daa8ab80d872d47de",
            "cf326f1d4ba6468898f838af4b1f244f",
            "8d7e5ad457ab4e3eab2e210237911ccc",
            "f63b1cfc09f54f72a96c1438eb00c439",
            "353ed5da968646f7a7071fb9ba10348b",
            "cd53fa65a25f479ea5cc2c2463d41225",
            "252bf0479a2f4d2e98b4cd04e7e1d25c"
          ]
        },
        "outputId": "01acffb5-7b53-4b97-f439-3d24980773f3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extract_VS:   0%|                                                                                             …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "609ca39a6d414fb7b51b7bde0df9aee9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extract_LAB:   0%|                                                                                            …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "35fd672ed29f42f1809318b67f12edd3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# extracted_features = extract_features()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import gzip"
      ],
      "metadata": {
        "id": "eIuZed5Vt0eL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with gzip.open('/content/gdrive/MyDrive/sepsis_model_weight/extracted_features_1112.pickle','rb') as f:\n",
        "    extracted_features = pickle.load(f)\n",
        "\n",
        "extra_features.set_index('hadm_id', drop=True, append=False, inplace=True, verify_integrity=False)\n",
        "extra_features=extra_features[['weight','age']]"
      ],
      "metadata": {
        "id": "_eNEjS4-k6Kt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "extra_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "X2E2J_6yvt8B",
        "outputId": "eddd47a3-c477-403b-edce-e3e60050b42c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             weight  age\n",
              "hadm_id                 \n",
              "27370071  89.000000   53\n",
              "27939019  89.000000   44\n",
              "27658791  48.700000   91\n",
              "21077260  62.100000   43\n",
              "21187549  62.100000   43\n",
              "...             ...  ...\n",
              "21874598  67.800000   62\n",
              "24735133  67.800000   61\n",
              "27024433  63.400000   23\n",
              "27555293  63.400000   26\n",
              "24296378  82.893457   78\n",
              "\n",
              "[105622 rows x 2 columns]"
            ],
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              "\n",
              "  <div id=\"df-fd6c6e8f-b2ba-4b5c-ac99-5b92c19918c4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hadm_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>67.800000</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
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              "      <th>27024433</th>\n",
              "      <td>63.400000</td>\n",
              "      <td>23</td>\n",
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              "    <tr>\n",
              "      <th>27555293</th>\n",
              "      <td>63.400000</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24296378</th>\n",
              "      <td>82.893457</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>105622 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd6c6e8f-b2ba-4b5c-ac99-5b92c19918c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \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",
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              "      fill: #1967D2;\n",
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              "    }\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-fd6c6e8f-b2ba-4b5c-ac99-5b92c19918c4 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-fd6c6e8f-b2ba-4b5c-ac99-5b92c19918c4');\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": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# with gzip.open('/content/gdrive/MyDrive/sepsis_model_weight/extracted_features.pickle','rb') as f:\n",
        "#     extracted_features = pickle.load(f)"
      ],
      "metadata": {
        "id": "5lYSignRQ_M-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GUHXlnCqNTjo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "11c12a2002264e18891cac1bc0b33298",
            "2f65d3b5631f4141b99ebddd00daf7b0",
            "01d91714606043bf8be4eb1326b2ed19",
            "590913d552134a8fbc0485bd98165e3b",
            "f8e0d836d2eb45dc833f3191054ee899",
            "64a2d6ef96964099a10b76bc6545a1d5",
            "0833c5bb575b46d29cb2ca355ec72884",
            "27ac396a9fd843be80a6a539ae521919",
            "1585f574e8ae4ed084cbc11f0c08c710",
            "81b561af84d9448a90c7b523f36c7f74",
            "dfcc8a97dcd147f9925ed93993d96f59"
          ]
        },
        "outputId": "657b44f7-8336-44e1-f752-2f7180974c8e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Get Death Label: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11c12a2002264e18891cac1bc0b33298"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 사망 레이블 하기\n",
        "death_labels = dict()\n",
        "admissions = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_der/sepsis3_admissions.csv', 'r', encoding='utf-8'))\n",
        "for row in tqdm(admissions, desc='Get Death Label'):\n",
        "  hadm_id = row['hadm_id']\n",
        "  death_yn = int(row['hospital_expire_flag'])\n",
        "  if death_yn:\n",
        "    event_time = str2datetime(row['deathtime'])\n",
        "  else:\n",
        "    event_time = str2datetime(row['dischtime'])\n",
        "  death_labels[hadm_id] = dict({'DEATH': death_yn, 'EVENT_TIME': event_time})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PtXfAzo0i7Hq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "31b1e83bfecd47eebebfd779abb5db8c",
            "512b66c4566c48bc8c3af1dbca11dc00",
            "33b6b24a4eb442fe85de25f20c75db65",
            "72f4322d96f94622bbe652cdc6bc6bd5",
            "3e13250f21844d0f9324a4bed8912c25",
            "4a9dfaaa15f0434fab46493711e37357",
            "f91e4fc4c6ac43e6b963caa1a2dbc482",
            "88cf7c90b1104e66a7d704cd8fe99d3f",
            "d5a2a85e272e4f9693eea6d9587d3abd",
            "e9f6ecff840443e5b28a24f339756fc4",
            "b2b82c688a584cef9d697718e23a425f"
          ]
        },
        "outputId": "fb29c0ba-d77c-4968-f67e-e14fea9aeae2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Get loss Label: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31b1e83bfecd47eebebfd779abb5db8c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 재실 기간 레이블 하기\n",
        "los_labels = dict()\n",
        "admissions = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_der/sepsis3_admissions.csv', 'r', encoding='utf-8'))\n",
        "for row in tqdm(admissions, desc='Get loss Label'):\n",
        "  hadm_id = row['hadm_id']\n",
        "  admin_time = str2datetime(row['admittime'])\n",
        "  disc_time = str2datetime(row['dischtime'])\n",
        "  los = (disc_time - admin_time).total_seconds() / (60*60)\n",
        "  los_labels[hadm_id] = int(los)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zm7qGtNPOH3p"
      },
      "outputs": [],
      "source": [
        "## 환자의 입실 퇴실 시간 정보 가져오기\n",
        "Extracted_HADMIDS = list(extracted_features.keys())\n",
        "admin_disc_time = dict()\n",
        "\n",
        "admissions = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_der/sepsis3_admissions.csv', 'r', encoding='utf-8'))\n",
        "for row in admissions:\n",
        "  hadm_id = row['hadm_id']\n",
        "  admin_time = str2datetime(row['admittime'])\n",
        "  disc_time = str2datetime(row['dischtime'])\n",
        "\n",
        "  if (admin_time != NOT_CONVERTED) and (disc_time != NOT_CONVERTED):\n",
        "    admin_disc_time[hadm_id] = dict({'ADMIN': admin_time, 'DISC': disc_time})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Auy7XQu0N5cN"
      },
      "outputs": [],
      "source": [
        "## 입실 시점으로부터 1시간 간격으로 데이터를 읽어오는 함수 정의\n",
        "def quantize_features(feature_list, admin_time, disc_time, missing_feature=False):\n",
        "  total_hours = int((disc_time - admin_time).total_seconds() / (60*60))\n",
        "  #입실 이후 최대 24시간 동안의 데이터만 가져오기\n",
        "  if total_hours > 24:\n",
        "    total_hours = 24\n",
        "  \n",
        "  quantized_features = list()\n",
        "  if missing_feature:\n",
        "    for i in range(total_hours):\n",
        "      quantized_time = admin_time + timedelta(hours=i)\n",
        "      quantized_value = np.nan #지금은 dic 형태라, pandas 쓰면 NAN이랑 동일, carry forward 도 가능\n",
        "      quantized_features.append((quantized_time, quantized_value))\n",
        "  else:\n",
        "    feature_list = list(sorted(feature_list, key=lambda x: x[0]))\n",
        "    for i in range(total_hours):\n",
        "      quantized_time = admin_time + timedelta(hours=i)\n",
        "      quantized_time_start = quantized_time - timedelta(hours=1)\n",
        "      quantized_values = list(filter(lambda x: quantized_time_start < x[0] <= quantized_time, feature_list)) #filter함수 True만 필터\n",
        "      if len(quantized_values) > 0:\n",
        "        quantized_value = quantized_values[-1][1]\n",
        "      else:\n",
        "        quantized_value = np.nan \n",
        "\n",
        "      quantized_features.append((quantized_time, quantized_value))\n",
        "  \n",
        "  return quantized_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SxMcge9WRy-m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b214f5f3be4a4815aaf10b09ca528c60",
            "a51c67a985394346ab40b33c6827ce98",
            "c9fb1f0ca37f4030932fa29f6504d01d",
            "56020ca539ac49698a10aa6769a66daf",
            "2ab3751ae0ff4df19aa1cf162b26098b",
            "36ffd5fa9a234121852cf7ddb4d4129a",
            "4d89791c372245dbb3466f03229b4c9b",
            "169200584582434c8126a708fd1d292d",
            "3714aa4da32940438d86196286bcfe66",
            "7681dd36895948348891f282715850bf",
            "fdb09973bb174c45bd719dba65d0bb23"
          ]
        },
        "outputId": "189b51d6-2b24-4753-a8fa-50fb284d52f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Doing Quantization:   0%|          | 0/32365 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b214f5f3be4a4815aaf10b09ca528c60"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "## 전체 데이터에 대해서 데이터 변환하기\n",
        "quantized_features = dict()\n",
        "for hadm_id in tqdm(Extracted_HADMIDS, desc='Doing Quantization'):\n",
        "    admin_time = admin_disc_time[hadm_id]['ADMIN']\n",
        "    disc_time = admin_disc_time[hadm_id]['DISC']\n",
        "    if (disc_time - admin_time).total_seconds() / (60*60) < 1.0:\n",
        "      continue\n",
        "    quantized_features[hadm_id] = dict()\n",
        "    for kk in feature_keys:\n",
        "      if kk in extracted_features[hadm_id]:\n",
        "        feature_list = extracted_features[hadm_id][kk]\n",
        "        quantized_features[hadm_id][kk] = quantize_features(feature_list, admin_time, disc_time)\n",
        "      else:\n",
        "        quantized_features[hadm_id][kk] = quantize_features(None, admin_time, disc_time, missing_feature=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "th7CkR4pSJE8"
      },
      "outputs": [],
      "source": [
        "def carry_forward(feature_name, feature_list):\n",
        "  imputed_features = list()\n",
        "  for i, (feature_t, feature_v) in enumerate(feature_list):\n",
        "    if np.isnan(feature_v):\n",
        "      last_observations = list(filter(lambda x: (x[0] <= feature_t) and (not np.isnan(x[1])), feature_list))\n",
        "      if len(last_observations) > 0:\n",
        "        imputed_features.append((feature_t, last_observations[-1][1]))\n",
        "      else:\n",
        "        imputed_features.append((feature_t, NORMAL_VALUE[feature_name]))\n",
        "    else:\n",
        "      imputed_features.append((feature_t, feature_v))\n",
        "\n",
        "  return imputed_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uhmbOBT_Shgz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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          ]
        },
        "outputId": "62c53b95-ec91-4445-ad19-673d6029bf15"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Carry-Forward:   0%|          | 0/32340 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0a955560a54445bb723f358750f5b78"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "Quantized_HADMIDS = list(quantized_features.keys())\n",
        "carry_forwarded_features = dict()\n",
        "for hadm_id in tqdm(Quantized_HADMIDS, desc='Carry-Forward'):\n",
        "  carry_forwarded_features[hadm_id] = dict()\n",
        "\n",
        "  for kk in feature_keys:\n",
        "    feature_list = quantized_features[hadm_id][kk]\n",
        "    carry_forwarded_features[hadm_id][kk] = carry_forward(kk, feature_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uOusHtWwTGAV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "outputId": "ea03c372-84f5-4603-972f-afc740b724ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Min-Max Normalization:   0%|                                                                                  …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "deb1f127eaf8472c92cbd4ce8aed8c9b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Min-Max Normalization\n",
        "min_max_normalized = dict()\n",
        "for hadm_id in tqdm(Quantized_HADMIDS, desc='Min-Max Normalization', ncols=700):\n",
        "  min_max_normalized[hadm_id] = dict()\n",
        "  for kk in feature_keys:\n",
        "    features = np.array(carry_forwarded_features[hadm_id][kk])\n",
        "\n",
        "    min = EXTRACT_FEATURE_CODES_NORMAL[kk][0]\n",
        "    max = EXTRACT_FEATURE_CODES_NORMAL[kk][1]\n",
        "\n",
        "    features[:, 1] = (features[:, 1] - min) / (max - min)\n",
        "    min_max_normalized[hadm_id][kk] = features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tg6sx0ytTLzE"
      },
      "outputs": [],
      "source": [
        "total_death_included_hadm_ids = list(death_labels.keys())\n",
        "total_los_included_hadm_ids = list(los_labels.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GLGBtCnTk0XN",
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "outputId": "628769c0-b992-4394-e503-e2d91c83d6e3",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Make Death Dataset:   0%|                                                                                     …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b30c52256e6548808338d46dc56fba88"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "+ Extra_features:   0%|                                                                                       …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a357fb382bf44b1fb813e819e8a671d8"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@title ##사망용 데이터 만들기\n",
        "\n",
        "death_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list(),\n",
        "    'EXTRA' : list(),\n",
        "})\n",
        "\n",
        "for hadm_id in tqdm(total_death_included_hadm_ids, desc='Make Death Dataset', ncols=700):\n",
        "  if hadm_id not in min_max_normalized:\n",
        "    continue\n",
        "  if len(min_max_normalized[hadm_id][feature_keys[0]]) >= 24:\n",
        "    time_len = 24\n",
        "  else:\n",
        "    time_len = len(min_max_normalized[hadm_id][feature_keys[0]])\n",
        "  \n",
        "  try:\n",
        "    data = np.array(min_max_normalized[hadm_id][feature_keys[0]])[:time_len, 1].reshape(time_len, 1)\n",
        "  except:\n",
        "    continue\n",
        "\n",
        "  for kk in feature_keys[1:]:\n",
        "    data = np.concatenate((data, np.array(min_max_normalized[hadm_id][kk])[:time_len, 1].reshape(time_len, 1)), axis=1)\n",
        "  \n",
        "  death_dataset['HADM_IDS'].append(hadm_id)\n",
        "  death_dataset['LABEL'].append(death_labels[hadm_id]['DEATH'])\n",
        "  death_dataset['DATA'].append(data)\n",
        "\n",
        "# 추가 feature 입력\n",
        "extra_features=extra_features.reindex(pd.to_numeric(death_dataset['HADM_IDS']))\n",
        "extra_features=np.array(extra_features)\n",
        "\n",
        "for ef in tqdm(extra_features, desc='+ Extra_features', ncols=700):\n",
        "  death_dataset['EXTRA'].append(ef)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title ## 항생제 레이블 하기\n",
        "anti_labels = dict()\n",
        "for row in list(set(id['hadm_id'])):\n",
        "    hadm_id = row\n",
        "    anti_1 = 0\n",
        "    anti_2 = 0\n",
        "    anti_3 = 0\n",
        "    anti_4 = 0\n",
        "    anti_5 = 0\n",
        "    temp = id[id['hadm_id']==row]\n",
        "    temp = temp.groupby(['LABEL']).count()\n",
        "    if 'cepha' in temp.iloc[:,0] : \n",
        "        anti_1 = 1\n",
        "    if 'glyco' in temp.iloc[:,0] :\n",
        "        anti_2 = 1\n",
        "    if 'peni' in temp.iloc[:,0] :\n",
        "        anti_3 = 1\n",
        "    if 'fluoro' in temp.iloc[:,0] :\n",
        "        anti_4 = 1\n",
        "    if 'macro' in temp.iloc[:,0] :\n",
        "        anti_5 = 1\n",
        "    anti_labels[hadm_id] = dict({'cepha': anti_1, 'glyco':anti_2, 'peni':anti_3, 'fluoro':anti_4, 'macro':anti_5})"
      ],
      "metadata": {
        "id": "s3u-awrDtM8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "cellView": "form",
        "outputId": "359d8d6c-5f77-4b8c-d77b-945697dcb130"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-47f4aadb86bb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    @ # 항생제 레이블 하기\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title ## 항생제용 데이터 만들기\n",
        "anti_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL_1': list(),\n",
        "    'LABEL_2': list(),\n",
        "    'LABEL_3': list(),\n",
        "    'LABEL_4': list(),\n",
        "    'LABEL_5': list(),\n",
        "    'DATA': list(),\n",
        "    'EXTRA' : list()\n",
        "})\n",
        "\n",
        "for hadm_id in tqdm(total_death_included_hadm_ids, desc='Make anti Dataset', ncols=700):\n",
        "  if hadm_id not in min_max_normalized:\n",
        "    continue\n",
        "  if len(min_max_normalized[hadm_id][feature_keys[0]]) >= 24:\n",
        "    time_len = 24\n",
        "  else:\n",
        "    time_len = len(min_max_normalized[hadm_id][feature_keys[0]])\n",
        "  \n",
        "  try:\n",
        "    data = np.array(min_max_normalized[hadm_id][feature_keys[0]])[:time_len, 1].reshape(time_len, 1)\n",
        "  except:\n",
        "    continue\n",
        "\n",
        "  for kk in feature_keys[1:]:\n",
        "    data = np.concatenate((data, np.array(min_max_normalized[hadm_id][kk])[:time_len, 1].reshape(time_len, 1)), axis=1)\n",
        "  \n",
        "  anti_dataset['HADM_IDS'].append(hadm_id)\n",
        "  try :\n",
        "      anti_dataset['LABEL_1'].append(anti_labels[hadm_id]['cepha'])\n",
        "  except : \n",
        "      anti_dataset['LABEL_1'].append(0)\n",
        "  try :\n",
        "      anti_dataset['LABEL_2'].append(anti_labels[hadm_id]['glyco'])\n",
        "  except : \n",
        "      anti_dataset['LABEL_2'].append(0)\n",
        "  try :\n",
        "      anti_dataset['LABEL_3'].append(anti_labels[hadm_id]['peni'])\n",
        "  except : \n",
        "      anti_dataset['LABEL_3'].append(0)\n",
        "  try :\n",
        "      anti_dataset['LABEL_4'].append(anti_labels[hadm_id]['fluoro'])\n",
        "  except : \n",
        "      anti_dataset['LABEL_4'].append(0)\n",
        "  try :\n",
        "      anti_dataset['LABEL_5'].append(anti_labels[hadm_id]['macro'])\n",
        "  except : \n",
        "      anti_dataset['LABEL_5'].append(0)\n",
        "\n",
        "  anti_dataset['DATA'].append(data)\n",
        "\n",
        "# 추가 feature 입력\n",
        "if type(extra_features) is np.ndarray : \n",
        "    pass\n",
        "else :\n",
        "    extra_features=extra_features.reindex(pd.to_numeric(anti_dataset['HADM_IDS']))\n",
        "    extra_features=np.array(extra_features)\n",
        "\n",
        "for ef in tqdm(extra_features, desc='+ Extra_features', ncols=700):\n",
        "  anti_dataset['EXTRA'].append(ef)\n",
        "\n",
        "print(len(anti_dataset['HADM_IDS']))"
      ],
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        {
          "output_type": "display_data",
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            "text/plain": [
              "Make anti Dataset:   0%|                                                                                      …"
            ],
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              "+ Extra_features:   0%|                                                                                       …"
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          "text": [
            "32340\n"
          ]
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