20221101_code.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/20221101_code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "xCBwZEG88iTZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cdffa30-441d-4cfe-85b9-3bdeeb6a16bb"
      },
      "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": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKfa-fAw8qIn",
        "outputId": "679a9397-1e5b-4e87-dfc7-41bcfa973d46"
      },
      "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",
      "execution_count": null,
      "metadata": {
        "id": "u9muJg8fLS_K"
      },
      "outputs": [],
      "source": [
        "Total_HADMIDS = set(sepsis['hadm_id'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xD5AA-mHrl4B"
      },
      "outputs": [],
      "source": [
        "feature_keys = ['FiO2', 'HR', 'PaO2', 'SBP', 'Temperature','Creatinine','Glucose', 'Hb', 'Lactacte', 'Plt','Sodium' ,'WBC']"
      ]
    },
    {
      "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",
        "    })\n",
        "EXTRACT_FEATURE_CODES_LAB = dict({\n",
        "    '220615': 'Creatinine', '229761': 'Creatinine',\n",
        "    '220621': 'Glucose', '225664': 'Glucose', '226537': 'Glucose',\n",
        "    '220228': 'Hb',\n",
        "    '225668': 'Lactacte',\n",
        "    '227457': 'Plt',\n",
        "    '220645': 'Sodium', '226534': 'Sodium',\n",
        "    '220546': 'WBC'}) \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",
        "        'Lactatce': [0.5, 2.5],\n",
        "        'Plt': [10, 500],\n",
        "        'Sodium': [90, 170],\n",
        "        'WBC': [1, 30]\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",
        "        'Lactatce': 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",
        "})\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=7241351, 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=615328, 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": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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          ]
        },
        "id": "8IuFnUqELI10",
        "outputId": "3465d602-920e-4bfe-c5b4-9616499bf998"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e819231cc3d34535adb749d68e1b2dbb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extract_VS:   0%|                                                                                             …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8541b31230234ee3b094eacbfd16aa0e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extract_LAB:   0%|                                                                                            …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "extracted_features = extract_features()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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          ]
        },
        "id": "GUHXlnCqNTjo",
        "outputId": "6d24fec2-ac72-413e-82f3-b896814614c9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99a409963f2e4bd28f8b6cb935971a90",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Get Death Label: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "id": "PtXfAzo0i7Hq",
        "outputId": "582843ac-68a5-40e0-d628-2a6aaa908fd7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3a814315b42c43f4a59cd571186189f6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Get loss Label: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9debacc05af94edbaee6d215adce9028",
            "c43951b1593c4b69af720286ceacf623",
            "47ee97ea6f424167a79ae31e3b084e2b",
            "717dd6e408294aa995460682eb8bda6e",
            "fb9ae9afd01942c48ea79f999629943e",
            "daed62cfa52945a5b8ba2d4729f7d467",
            "867f5717f32442eca389a33478203f7b",
            "8560981b4fe544018079359126904fb1",
            "2747112928424905afdc9f1af291e344",
            "29067893497e418b848a0055ac5049d3",
            "54c37704080346ffbceaaa08cbab6e7d"
          ]
        },
        "id": "SxMcge9WRy-m",
        "outputId": "d537240a-3cb2-447f-fa11-74507964e28e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9debacc05af94edbaee6d215adce9028",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Doing Quantization:   0%|          | 0/32812 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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)\n"
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "eae23d3ecca048dfbe0972630280425e",
            "b1338e8b13d340bfbd5923558b12d9af",
            "906bb79f38314401b11ae2932482d53a",
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            "387570612c284612888c0e020f28cbf6",
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            "79d42ecf68984391b49b58c4238085de",
            "57d69c105ef44ecdaed386ac4063697d",
            "837fbb0216e74cf889e32af4a77d9dc9",
            "35aa6271461d4529b669e4327b6433ef",
            "01ca384bcbb145f1885c96da3eb83b82"
          ]
        },
        "id": "uhmbOBT_Shgz",
        "outputId": "bc02628b-f9aa-4b35-920a-b4b8857e3362"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eae23d3ecca048dfbe0972630280425e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Carry-Forward:   0%|          | 0/32785 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fffe02e0eacb404580aa1de153e0a157",
            "7bc518e6d26146c98226a28a2a7363ef",
            "dba7241eee804b56b52f010b75401fc1",
            "276fc4ae40f64a1696b2843e5e10c116",
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            "a24e7ccb9b1c4659ab6453c5e854631c",
            "e41cedbe0cd443efb48c5d3c2e107ce5",
            "e44e6718c2ee4616b6aa380e03716466",
            "98004a3bdd344ef09983c8ffe04298af",
            "ef586eeca984410f8778eac0f736b40c",
            "e266f2c4a2fc4aa59686397701a72695"
          ]
        },
        "id": "uOusHtWwTGAV",
        "outputId": "00539bf6-34e6-403c-bae8-af97ec6c6236"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fffe02e0eacb404580aa1de153e0a157",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Min-Max Normalization:   0%|                                                                                  …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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 = FEATURE_NORMAL_RANGE[kk][0]\n",
        "    max = FEATURE_NORMAL_RANGE[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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "716537a7fcb2471e9857bba28c484cdd",
            "a5a2f326cf9946d89bddeaf0ccb714b1",
            "62681f4b05e74186b91489e682150fe4",
            "e870357a5c7a41db89ffa596e99f6b3d",
            "fea6c742f7dd46ea8836080c90079aaa",
            "5ce939731df9495bb4c430157f35a2d5",
            "650a30f0863746318784885af11cef7b",
            "4de03ebccf714493b1d0fcae20bfdc8b",
            "91a9e58cbbbf4b3aae6e4e161713b3b9",
            "b99411094dc5458d850b8aa17649e2e5",
            "6341951516234d30a3a9dd900856db6f"
          ]
        },
        "id": "GLGBtCnTk0XN",
        "outputId": "5fd32927-a238-4fc8-fdf4-380d359aaf7b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "716537a7fcb2471e9857bba28c484cdd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Make Death Dataset:   0%|                                                                                     …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "## 사망 예측용 데이터\n",
        "death_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': 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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "120ca2b8d38f411496c6bb72b653d090",
            "7af63be1ccd14f708b4b77f2fbb38b26",
            "c72a6d8b5cbf4c70a25bf1330c872d2e",
            "9041ccad9bb24822be512ba6e582ef09",
            "f0483655f18943e6a1631b62395b704a",
            "af624fee48934f2a9c4001b27ad5faa7",
            "2cfabc724ae64c2ab78a2383f47ec6a9",
            "6c507a638fb84705b680d22db7b20d27",
            "f7bb27ca58704a0c93f03e31ae1dcddd",
            "07194774c7d541eea5e749d98dce11c3",
            "89ebf360b3f74a17829e4cf762d4dfbd"
          ]
        },
        "id": "jQUuy4y9qEjl",
        "outputId": "32025bc8-db78-4669-f01e-519fc456bf53"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "120ca2b8d38f411496c6bb72b653d090",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Make LOS Dataset:   0%|                                                                                       …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "## 재실기간 예측용 데이터\n",
        "los_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list()\n",
        "})\n",
        "\n",
        "for hadm_id in tqdm(total_los_included_hadm_ids, desc='Make LOS Dataset', ncols=700):\n",
        "  time_len = 12\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",
        "  los_dataset['HADM_IDS'].append(hadm_id)\n",
        "  los_dataset['LABEL'].append(los_labels[hadm_id])\n",
        "  los_dataset['DATA'].append(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78GmjAzpqJWt"
      },
      "source": [
        "## train-test 분할"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o-6aOPFhqFQ_"
      },
      "outputs": [],
      "source": [
        "## 학습용 검증용 데이터 나누기 (8:2)\n",
        "def split_train_test(dataset, train_ratio=0.8, pos_neg=True):\n",
        "  #레이블이 양성, 음성인 경우, 학습 데이터, 테스트 데이터를 나눌 때 \n",
        "  #양성, 음성의 비율을 각각 유지할 수 있도록 구현\n",
        "  if pos_neg:\n",
        "    pos_idx = np.where(np.array(dataset['LABEL']) == 1)[0]\n",
        "    neg_idx = np.where(np.array(dataset['LABEL']) == 0)[0]\n",
        "\n",
        "    # suffle 넣어줘서 index 섞어줘야함. np.random, random,suffle\n",
        "    np.random.shuffle(pos_idx)\n",
        "    np.random.shuffle(neg_idx)\n",
        "\n",
        "    pos_num = len(pos_idx)\n",
        "    neg_num = len(neg_idx)\n",
        "\n",
        "    train_pos_idx = list(pos_idx[:int(pos_num * train_ratio)])\n",
        "    test_pos_idx = list(pos_idx[int(pos_num * train_ratio):])\n",
        "\n",
        "    train_neg_idx = list(neg_idx[:int(neg_num * train_ratio)])\n",
        "    test_neg_idx = list(neg_idx[int(neg_num * train_ratio):])\n",
        "\n",
        "    train_idx = train_pos_idx + train_neg_idx\n",
        "    test_idx = test_pos_idx + test_neg_idx\n",
        "  \n",
        "  #레이블이 양성, 음성이 아닌경우 단순하게 전체 데이터에서 학습, 테스트 비율만큼 나누기\n",
        "  else:\n",
        "    idx = np.arange(len(dataset['LABEL']))\n",
        "    np.random.shuffle(idx)\n",
        "    train_idx = idx[:int(len(idx) * train_ratio)]\n",
        "    test_idx = idx[int(len(idx) * train_ratio):]\n",
        "\n",
        "  train_set = dict({\n",
        "      'HADM_IDS': np.array(dataset['HADM_IDS'])[train_idx],\n",
        "      'LABEL': np.array(dataset['LABEL'])[train_idx],\n",
        "      'DATA': np.array(dataset['DATA'])[train_idx]\n",
        "  })\n",
        "\n",
        "  test_set = dict({\n",
        "      'HADM_IDS': np.array(dataset['HADM_IDS'])[test_idx],\n",
        "      'LABEL': np.array(dataset['LABEL'])[test_idx],\n",
        "      'DATA': np.array(dataset['DATA'])[test_idx]\n",
        "  })\n",
        "  return train_set, test_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GI5h60FIqQXK",
        "outputId": "e30668a9-0c1a-4dc7-dec0-54a0f6904eb8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:35: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:41: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"
          ]
        }
      ],
      "source": [
        "death_train, death_test = split_train_test(death_dataset)\n",
        "los_train, los_test = split_train_test(los_dataset, pos_neg=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgYq2iiPqSZj",
        "outputId": "aa873987-56bd-4428-a8ed-0d9e1853241f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "26227\n",
            "6558\n",
            "26072\n",
            "6519\n"
          ]
        }
      ],
      "source": [
        "print(len(death_train['HADM_IDS']))\n",
        "print(len(death_test['HADM_IDS']))\n",
        "print(len(los_train['HADM_IDS']))\n",
        "print(len(los_test['HADM_IDS']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZvM3xUVqF5A"
      },
      "source": [
        "## 모델 학습 \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GP_zI7WqFzf"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b38QBa6_TYQm"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import math\n",
        "from tensorflow.keras.utils import Sequence\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jtelKrDCpoOL"
      },
      "outputs": [],
      "source": [
        "## 사망예측 모델용 데이터 로더\n",
        "class Death_Dataloader(Sequence):\n",
        "    def __init__(self, x_set, y_set, hadm_id_set, batch_size, shuffle=False):\n",
        "        self.x, self.y, self.hadm_id = x_set, y_set, hadm_id_set\n",
        "        self.batch_size = batch_size\n",
        "        self.shuffle = shuffle\n",
        "        self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "        return math.ceil(len(self.x) / self.batch_size)\n",
        "\n",
        "\t\t# batch 단위로 직접 묶어줘야 함\n",
        "    def __getitem__(self, idx):\n",
        "\t\t# sampler의 역할(index를 batch_size만큼 sampling해줌)\n",
        "        indices = self.indices[idx*self.batch_size:(idx+1)*self.batch_size]\n",
        "\n",
        "        batch_x = [self.x[i] for i in indices]\n",
        "        ## 입력 데이터들의 크기를 최대 24시간으로 통일하고 24시간 보다 짧은 데이터들의 경우 끝에 '0' padding 붙이기\n",
        "        padded_batch_x = np.zeros((len(batch_x), 24, len(feature_keys)))\n",
        "        for i in range(len(batch_x)):\n",
        "          valid_length = len(batch_x[i])\n",
        "          padded_batch_x[i][:valid_length, :] = batch_x[i]\n",
        "        batch_y = [self.y[i] for i in indices]\n",
        "        batch_hadm_id = [self.hadm_id[i] for i in indices]\n",
        "\n",
        "        return np.array(padded_batch_x).astype('float32'), np.array(batch_y).astype('float32')\n",
        "\n",
        "    # epoch이 끝날때마다 실행\n",
        "    def on_epoch_end(self):\n",
        "        self.indices = np.arange(len(self.x))\n",
        "        if self.shuffle == True:\n",
        "            np.random.shuffle(self.indices)\n",
        "\n",
        "# batch 2의 64,128\n",
        "# resampled_ds = tf.data.experimental.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])\n",
        "# resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)\n",
        "death_train_loader = Death_Dataloader(death_train['DATA'], death_train['LABEL'], death_train['HADM_IDS'], 125, True)\n",
        "death_test_loader = Death_Dataloader(death_test['DATA'], death_test['LABEL'], death_test['HADM_IDS'], 125, False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LcBGvAS7kjg8"
      },
      "source": [
        ":"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c8GVk6HHTTlz"
      },
      "outputs": [],
      "source": [
        "# #death_train['DATA'][0].shape\n",
        "# for i in death_train_loader:\n",
        "#     print(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8fQJzXzkUnl5"
      },
      "outputs": [],
      "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_fluoro = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_fluoro.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_macro = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_macro.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)"
      ]
    },
    {
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          "height": 424
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        "id": "lvP-e_o2XmpP",
        "outputId": "7a0826d6-1239-4aa2-c486-53613a69c5f8"
      },
      "outputs": [
        {
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            "text/plain": [
              "        hadm_id   stay_id\n",
              "0      23836605  30001947\n",
              "1      28841024  30002415\n",
              "2      24622512  30002548\n",
              "3      20012928  30003226\n",
              "4      24753602  30003275\n",
              "...         ...       ...\n",
              "15815  27586853  39994129\n",
              "15816  29577504  39996073\n",
              "15817  27161460  39999384\n",
              "15818  26910235  39999552\n",
              "15819  25335698  39999562\n",
              "\n",
              "[15820 rows x 2 columns]"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diagnoses_icd = pd.merge(left = diagnoses_icd, right = sepsis[['subject_id','sepsis3']], how='inner', left_on='subject_id', right_on='subject_id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bn7zfO5e_kRw",
        "outputId": "187e22db-03c2-47d7-83db-9964fb5a54ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 25)                4200      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 125)               3250      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 126       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,576\n",
            "Trainable params: 7,576\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "## 사망예측 모델\n",
        "death_lstm = keras.Sequential([\n",
        "    layers.LSTM(25, input_shape=(13,16)),\n",
        "    layers.Dense(125, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "death_lstm.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7BtKf5g_t7c",
        "outputId": "a5458e80-e3cc-4bc9-f4e8-6e1f30981b14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "210/210 - 10s - loss: 0.4405 - accuracy: 0.8325 - val_loss: 0.4190 - val_accuracy: 0.8521 - 10s/epoch - 47ms/step\n",
            "Epoch 2/50\n",
            "210/210 - 1s - loss: 0.4156 - accuracy: 0.8516 - val_loss: 0.4186 - val_accuracy: 0.8518 - 1s/epoch - 5ms/step\n",
            "Epoch 3/50\n",
            "210/210 - 1s - loss: 0.4144 - accuracy: 0.8515 - val_loss: 0.4156 - val_accuracy: 0.8521 - 1s/epoch - 5ms/step\n",
            "Epoch 4/50\n",
            "210/210 - 1s - loss: 0.4127 - accuracy: 0.8516 - val_loss: 0.4133 - val_accuracy: 0.8518 - 1s/epoch - 5ms/step\n",
            "Epoch 5/50\n",
            "210/210 - 1s - loss: 0.4106 - accuracy: 0.8516 - val_loss: 0.4149 - val_accuracy: 0.8518 - 1s/epoch - 5ms/step\n",
            "Epoch 6/50\n",
            "210/210 - 1s - loss: 0.4105 - accuracy: 0.8518 - val_loss: 0.4130 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 7/50\n",
            "210/210 - 1s - loss: 0.4109 - accuracy: 0.8518 - val_loss: 0.4161 - val_accuracy: 0.8518 - 1s/epoch - 5ms/step\n",
            "Epoch 8/50\n",
            "210/210 - 1s - loss: 0.4094 - accuracy: 0.8521 - val_loss: 0.4114 - val_accuracy: 0.8521 - 1s/epoch - 5ms/step\n",
            "Epoch 9/50\n",
            "210/210 - 1s - loss: 0.4089 - accuracy: 0.8518 - val_loss: 0.4133 - val_accuracy: 0.8519 - 1s/epoch - 5ms/step\n",
            "Epoch 10/50\n",
            "210/210 - 1s - loss: 0.4088 - accuracy: 0.8519 - val_loss: 0.4130 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 11/50\n",
            "210/210 - 1s - loss: 0.4083 - accuracy: 0.8516 - val_loss: 0.4120 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 12/50\n",
            "210/210 - 1s - loss: 0.4079 - accuracy: 0.8525 - val_loss: 0.4117 - val_accuracy: 0.8519 - 1s/epoch - 5ms/step\n",
            "Epoch 13/50\n",
            "210/210 - 1s - loss: 0.4083 - accuracy: 0.8519 - val_loss: 0.4174 - val_accuracy: 0.8471 - 1s/epoch - 5ms/step\n",
            "Epoch 14/50\n",
            "210/210 - 1s - loss: 0.4075 - accuracy: 0.8516 - val_loss: 0.4118 - val_accuracy: 0.8518 - 1s/epoch - 5ms/step\n",
            "Epoch 15/50\n",
            "210/210 - 1s - loss: 0.4080 - accuracy: 0.8518 - val_loss: 0.4142 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 16/50\n",
            "210/210 - 1s - loss: 0.4073 - accuracy: 0.8520 - val_loss: 0.4127 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 17/50\n",
            "210/210 - 1s - loss: 0.4069 - accuracy: 0.8523 - val_loss: 0.4100 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 18/50\n",
            "210/210 - 1s - loss: 0.4075 - accuracy: 0.8522 - val_loss: 0.4124 - val_accuracy: 0.8513 - 1s/epoch - 5ms/step\n",
            "Epoch 19/50\n",
            "210/210 - 1s - loss: 0.4073 - accuracy: 0.8523 - val_loss: 0.4094 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 20/50\n",
            "210/210 - 1s - loss: 0.4063 - accuracy: 0.8524 - val_loss: 0.4087 - val_accuracy: 0.8529 - 1s/epoch - 5ms/step\n",
            "Epoch 21/50\n",
            "210/210 - 1s - loss: 0.4060 - accuracy: 0.8523 - val_loss: 0.4083 - val_accuracy: 0.8516 - 1s/epoch - 5ms/step\n",
            "Epoch 22/50\n",
            "210/210 - 1s - loss: 0.4071 - accuracy: 0.8519 - val_loss: 0.4089 - val_accuracy: 0.8529 - 1s/epoch - 5ms/step\n",
            "Epoch 23/50\n",
            "210/210 - 1s - loss: 0.4064 - accuracy: 0.8525 - val_loss: 0.4097 - val_accuracy: 0.8519 - 1s/epoch - 5ms/step\n",
            "Epoch 24/50\n",
            "210/210 - 1s - loss: 0.4069 - accuracy: 0.8526 - val_loss: 0.4093 - val_accuracy: 0.8527 - 1s/epoch - 5ms/step\n",
            "Epoch 25/50\n",
            "210/210 - 1s - loss: 0.4063 - accuracy: 0.8524 - val_loss: 0.4117 - val_accuracy: 0.8513 - 1s/epoch - 5ms/step\n",
            "Epoch 26/50\n",
            "210/210 - 1s - loss: 0.4061 - accuracy: 0.8529 - val_loss: 0.4091 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 27/50\n",
            "210/210 - 1s - loss: 0.4062 - accuracy: 0.8526 - val_loss: 0.4095 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 28/50\n",
            "210/210 - 1s - loss: 0.4059 - accuracy: 0.8529 - val_loss: 0.4085 - val_accuracy: 0.8529 - 1s/epoch - 5ms/step\n",
            "Epoch 29/50\n",
            "210/210 - 1s - loss: 0.4058 - accuracy: 0.8527 - val_loss: 0.4148 - val_accuracy: 0.8503 - 1s/epoch - 5ms/step\n",
            "Epoch 30/50\n",
            "210/210 - 1s - loss: 0.4059 - accuracy: 0.8523 - val_loss: 0.4070 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 31/50\n",
            "210/210 - 1s - loss: 0.4064 - accuracy: 0.8525 - val_loss: 0.4082 - val_accuracy: 0.8525 - 1s/epoch - 5ms/step\n",
            "Epoch 32/50\n",
            "210/210 - 1s - loss: 0.4055 - accuracy: 0.8525 - val_loss: 0.4095 - val_accuracy: 0.8530 - 1s/epoch - 5ms/step\n",
            "Epoch 33/50\n",
            "210/210 - 1s - loss: 0.4055 - accuracy: 0.8528 - val_loss: 0.4085 - val_accuracy: 0.8522 - 1s/epoch - 5ms/step\n",
            "Epoch 34/50\n",
            "210/210 - 1s - loss: 0.4045 - accuracy: 0.8528 - val_loss: 0.4073 - val_accuracy: 0.8535 - 1s/epoch - 5ms/step\n",
            "Epoch 35/50\n",
            "210/210 - 1s - loss: 0.4053 - accuracy: 0.8527 - val_loss: 0.4086 - val_accuracy: 0.8530 - 1s/epoch - 5ms/step\n",
            "Epoch 36/50\n",
            "210/210 - 1s - loss: 0.4052 - accuracy: 0.8529 - val_loss: 0.4081 - val_accuracy: 0.8524 - 1s/epoch - 5ms/step\n",
            "Epoch 37/50\n",
            "210/210 - 1s - loss: 0.4055 - accuracy: 0.8527 - val_loss: 0.4089 - val_accuracy: 0.8529 - 1s/epoch - 5ms/step\n",
            "Epoch 38/50\n",
            "210/210 - 1s - loss: 0.4050 - accuracy: 0.8531 - val_loss: 0.4075 - val_accuracy: 0.8533 - 1s/epoch - 5ms/step\n",
            "Epoch 39/50\n",
            "210/210 - 1s - loss: 0.4049 - accuracy: 0.8529 - val_loss: 0.4075 - val_accuracy: 0.8532 - 1s/epoch - 5ms/step\n",
            "Epoch 40/50\n",
            "210/210 - 1s - loss: 0.4049 - accuracy: 0.8528 - val_loss: 0.4070 - val_accuracy: 0.8532 - 1s/epoch - 5ms/step\n",
            "Epoch 41/50\n",
            "210/210 - 1s - loss: 0.4049 - accuracy: 0.8530 - val_loss: 0.4061 - val_accuracy: 0.8524 - 1s/epoch - 5ms/step\n",
            "Epoch 42/50\n",
            "210/210 - 1s - loss: 0.4047 - accuracy: 0.8537 - val_loss: 0.4063 - val_accuracy: 0.8535 - 1s/epoch - 5ms/step\n",
            "Epoch 43/50\n",
            "210/210 - 1s - loss: 0.4036 - accuracy: 0.8531 - val_loss: 0.4078 - val_accuracy: 0.8527 - 1s/epoch - 5ms/step\n",
            "Epoch 44/50\n",
            "210/210 - 1s - loss: 0.4045 - accuracy: 0.8526 - val_loss: 0.4101 - val_accuracy: 0.8516 - 1s/epoch - 5ms/step\n",
            "Epoch 45/50\n",
            "210/210 - 1s - loss: 0.4039 - accuracy: 0.8534 - val_loss: 0.4076 - val_accuracy: 0.8521 - 1s/epoch - 5ms/step\n",
            "Epoch 46/50\n",
            "210/210 - 1s - loss: 0.4039 - accuracy: 0.8531 - val_loss: 0.4086 - val_accuracy: 0.8530 - 1s/epoch - 5ms/step\n",
            "Epoch 47/50\n",
            "210/210 - 1s - loss: 0.4040 - accuracy: 0.8531 - val_loss: 0.4080 - val_accuracy: 0.8532 - 1s/epoch - 5ms/step\n",
            "Epoch 48/50\n",
            "210/210 - 1s - loss: 0.4052 - accuracy: 0.8531 - val_loss: 0.4073 - val_accuracy: 0.8529 - 1s/epoch - 5ms/step\n",
            "Epoch 49/50\n",
            "210/210 - 1s - loss: 0.4041 - accuracy: 0.8534 - val_loss: 0.4057 - val_accuracy: 0.8529 - 1s/epoch - 5ms/step\n",
            "Epoch 50/50\n",
            "210/210 - 1s - loss: 0.4032 - accuracy: 0.8533 - val_loss: 0.4069 - val_accuracy: 0.8524 - 1s/epoch - 5ms/step\n"
          ]
        }
      ],
      "source": [
        "## 사망예측 모델 학습\n",
        "death_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "num_epochs = 50\n",
        "history = death_lstm.fit(death_train_loader, validation_data=death_test_loader, epochs=num_epochs, verbose=2)"
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        "sum(death_train['LABEL'])/len(death_train['LABEL'])"
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