0.generate_tableOnePlus.ipynb

{
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   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import warnings\n",
    "from tqdm import tqdm\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xorbits.pandas as xpd\n",
    "import utils\n",
    "from utils import get_3periods, parse_man_info, quick_load_liuzhong_health_check_data\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79e6d5f0-76e9-4280-a339-ca6b2d6f21da",
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-10 09:26:19,861 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (100 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:19,879 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (110 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:19,893 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (120 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:19,908 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (130 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:19,922 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (140 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:19,936 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (150 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:19,950 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (160 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:22,636 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (100 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:22,777 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (110 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:22,787 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (120 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:22,798 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (130 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:22,810 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (140 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:22,821 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (150 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:22,832 xoscar.backends.core 3963377 WARNING  Actor caller has created too many clients (160 >= 100), the global router may not be set.\n",
      "2023-10-10 09:26:30,573 xorbits._mars.deploy.oscar.local 3963377 WARNING  Web service started at http://0.0.0.0:43802\n"
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       "  0%|          |   0.00/100 [00:00<?, ?it/s]"
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   ],
   "source": [
    "dir_path = \"/cluster/home/bqhu_jh/projects/healthman/\"\n",
    "\n",
    "df_merged_pq = xpd.read_parquet(f\"{dir_path}/analysis/detail.parquet\").rebalance()\n",
    "\n",
    "if not os.path.isfile(f\"{dir_path}/analysis/detail_rebalanced.parquet\"):\n",
    "    os.system(f\"mkdir -p {dir_path}/analysis/detail_rebalanced.parquet\")\n",
    "    df_merged_pq.to_parquet(f\"{dir_path}/analysis/detail_rebalanced.parquet\")\n",
    "\n",
    "df_merged_pq = xpd.read_parquet(f\"{dir_path}/analysis/detail_rebalanced.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d212f7b",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-10-10T01:28:10.031124Z"
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    "tags": []
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   "outputs": [],
   "source": [
    "l_cols = [\"default_num\"]\n",
    "dict_parse_func = {var: getattr(utils, \"parse_%s\" % var) for var in l_cols}\n",
    "def process_line(x):\n",
    "    var,result = x[\"item_id\"], x[\"results\"]\n",
    "    if var not in dict_parse_func:\n",
    "        var = \"default_num\"\n",
    "\n",
    "    return dict_parse_func[var](result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b49ddb87",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-10-10T01:28:10.473537Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "l_high_lighted = [\n",
    "    \"v000760\", \"v000761\", \"v002266\",\n",
    "    'v000763', 'v9319', 'v000762', 'v002161',\n",
    "    'HEART.Normal', 'HEART.SinusRhythm', 'HEART.T_change', 'HEART.SinusBradycardia',\n",
    "    'HEART.ST_change', 'HEART.SinusArrhythmia', 'HEART.ElectricAxisDeviation', 'HEART.LVH', 'HEART.SinusTachycardia',\n",
    "    'HEART.AtrialFibrillation', 'HEART.HeartBlocks', 'HEART.ExtraSystole',\n",
    "    \"LUNG.GroundGlassOpacity\", \"LUNG.Effusion\", \"LUNG.Fibrosis\", \"LUNG.Calcification\", \"LUNG.ProliferativeLesion\",\n",
    "    \"LUNG.SolidNodule\", \"LUNG.SmallNodule\", \"LUNG.Nodule\", \"LUNG.Normal\",\n",
    "    'v0009', 'v0008', 'v0010', 'v1235',\n",
    "    'v0023', 'v1970', 'v1164', 'v9046',\n",
    "    'v9047', 'v9048', 'v9049', 'v9050',\n",
    "    'v9057', 'v9064', 'v9045', 'v9058',\n",
    "    'v9059', 'v9060', 'v9061', 'v9062',\n",
    "    'v9063', 'v9065', 'v9066', 'v9051', 'v9055',\n",
    "    'v9089', 'v9090', 'v9091', 'v0017',\n",
    "    'v0045', 'v0022', 'v1162', 'v1940',\n",
    "    'v1925', 'LIVER.Steatosis', 'v9453', 'v0955',\n",
    "    'v9461', 'v9462', 'v9464', 'v0015', 'v0016', 'v0027', 'v0026', 'v0032'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5cafee",
   "metadata": {},
   "source": [
    "## 1. xdf_num_pvt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd1b72fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-10T01:28:10.476701Z",
     "iopub.status.busy": "2023-10-10T01:28:10.476521Z",
     "iopub.status.idle": "2023-10-10T01:28:10.928671Z",
     "shell.execute_reply": "2023-10-10T01:28:10.928292Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "l_numeric_col = l_high_lighted\n",
    "xdf_merged_pq_num = df_merged_pq[df_merged_pq[\"item_id\"].isin(l_numeric_col)]\n",
    "xdf_merged_pq_num[\"value\"] = xdf_merged_pq_num[[\"item_id\", \"results\"]].apply(lambda x: process_line(x), axis=1)\n",
    "xdf_num_pvt = xpd.pivot_table(xdf_merged_pq_num[[\"exam_id\", \"item_id\", \"value\"]], \n",
    "                        index=\"exam_id\", columns=\"item_id\", values=\"value\", aggfunc=lambda x: x.astype(float).mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceae6b0f",
   "metadata": {},
   "source": [
    "## 2. x_text_dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d367460a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-10-10T01:28:11.534040Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def detect_descriptions(x, descriptions):\n",
    "    if xpd.isna(x):\n",
    "        return x\n",
    "\n",
    "    for description in descriptions.split(\"|\"):\n",
    "        if re.search(description, x):\n",
    "            return 1\n",
    "\n",
    "    return 0\n",
    "\n",
    "\n",
    "def get_df_scan(df_merged, scan_items, dict_descriptions):\n",
    "    df_merged_scan = df_merged[df_merged[\"combine_item_name\"].isin(scan_items)][[\"exam_id\", \"conclusion\"]]\n",
    "    for k in dict_descriptions:\n",
    "        v = dict_descriptions[k]\n",
    "        df_merged_scan[v] = df_merged_scan[\"conclusion\"].apply(lambda x: detect_descriptions(x, k))\n",
    "\n",
    "    return xpd.melt(df_merged_scan.drop([\"conclusion\"], axis=1), id_vars=[\"exam_id\"])\n",
    "\n",
    "l_params = [\n",
    "    {\n",
    "        \"scan_items\" : [\"常规心电图检查\"],\n",
    "        \"dict_descriptions\" : {\n",
    "            \"正常心电图\": \"HEART.Normal\",\n",
    "            \"窦性心律;\": \"HEART.SinusRhythm\",\n",
    "            \"T波改变\": \"HEART.T_change\",\n",
    "            \"窦性心动过缓\": \"HEART.SinusBradycardia\",\n",
    "            \"ST段\" : \"HEART.ST_change\",\n",
    "            \"窦性心律不齐\": \"HEART.SinusArrhythmia\",\n",
    "            \"电轴\": \"HEART.ElectricAxisDeviation\",\n",
    "            \"左室高电压\": \"HEART.LVH\",\n",
    "            \"窦性心动过速\": \"HEART.SinusTachycardia\",\n",
    "            \"房颤|房性\" : \"HEART.AtrialFibrillation\",\n",
    "            \"传导阻滞\": \"HEART.HeartBlocks\",\n",
    "            \"早搏\" : \"HEART.ExtraSystole\",\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"scan_items\" : [\"肝胆脾胰（彩超）\", \"肝胆脾胰+双肾（彩超）\", \"胸部CT平扫\", \"肺部HR \\x01T平扫\"],\n",
    "        \"dict_descriptions\" : {\n",
    "            \"脂肪肝\": \"LIVER.Steatosis\",\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "        \"scan_items\" : [\"胸部CT平扫\", \"肺部CT平扫\",\"肺部HR \\x01T平扫\", \n",
    "                        \"数字化摄影(DR)\", \"+胸部CT平扫\", \"CT胸部平扫\", \"CT肺部HR平扫\"],\n",
    "        \"dict_descriptions\" : {\n",
    "            \"磨玻璃\": \"LUNG.GroundGlassOpacity\",\n",
    "            \"积液\": \"LUNG.Effusion\",\n",
    "            \"纤维\": \"LUNG.Fibrosis\",\n",
    "            \"钙化\": \"LUNG.Calcification\",\n",
    "            \"增殖灶\": \"LUNG.ProliferativeLesion\",\n",
    "            \"实性结节\": \"LUNG.SolidNodule\",\n",
    "            \"小结节\": \"LUNG.SmallNodule\",\n",
    "            \"结节\": \"LUNG.Nodule\",\n",
    "            \"未见明显异常\": \"LUNG.Normal\"\n",
    "        }\n",
    "    }\n",
    "]\n",
    "\n",
    "x_text_dfs = xpd.concat([\n",
    "            get_df_scan(df_merged_pq, param[\"scan_items\"], param[\"dict_descriptions\"]) for param in l_params \n",
    "]).pivot_table(index=\"exam_id\", columns=\"variable\", values=\"value\", aggfunc=np.max)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ccb5e7b",
   "metadata": {},
   "source": [
    "## 3. xdf_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a5dae79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-10T01:28:11.537209Z",
     "iopub.status.busy": "2023-10-10T01:28:11.537025Z",
     "iopub.status.idle": "2023-10-10T01:28:12.137979Z",
     "shell.execute_reply": "2023-10-10T01:28:12.137360Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def parse_main_pq(file_main_pq):\n",
    "    xdf_main = xpd.read_parquet(file_main_pq)\n",
    "    xdf_main[\"year\"] = xdf_main[\"exam_date\"].apply(lambda x: x.split(\"-\")[0]).astype(np.int32)\n",
    "    xdf_main[\"month\"] = xdf_main[\"exam_date\"].apply(lambda x: x.split(\"-\")[1]).astype(np.int32)\n",
    "    xdf_main[\"day\"] = xdf_main[\"exam_date\"].apply(lambda x: x.split(\"-\")[2]).astype(np.int32)\n",
    "    xdf_main[\"gender\"] = xdf_main[\"gender\"].apply(lambda x: \"male\" if x ==1 else \"female\" )\n",
    "    xdf_main[\"age\"] = xdf_main.apply(\n",
    "                                    lambda x: int(x[\"year\"])-int(x[\"birthday\"].split(\"-\")[0]), axis=1\n",
    "    )\n",
    "    return xdf_main\n",
    "\n",
    "\n",
    "xdf_main= parse_main_pq(f\"{dir_path}/analysis/main.parquet\")"
   ]
  },
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   "execution_count": 8,
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   "source": [
    "xdf_table1plus_pvt = xpd.melt(\n",
    "        xdf_main.join(xdf_num_pvt.join(x_text_dfs, how=\"outer\")).drop([\"exam_date\"], axis=1).rebalance(), \n",
    "        id_vars=[\"birthday\", \"year\", \"month\", \"day\", \"gender\", \"sample_id\"]\n",
    "    ).pivot_table(index=[\"birthday\", \"year\", \"month\", \"day\", \"gender\", \"sample_id\"], \n",
    "                columns=\"variable\", values=\"value\", aggfunc=np.mean\n",
    ")\n",
    "\n",
    "df_table1plus_final_allmonths = xdf_table1plus_pvt.reset_index()[\n",
    "        [\"birthday\", \"year\", \"month\", \"day\", \"gender\", \"sample_id\"] + l_high_lighted\n",
    "    ].to_pandas()\n",
    "\n",
    "df_table1plus_final = parse_man_info(\n",
    "    df_table1plus_final_allmonths, na_val=\"Control\"\n",
    ")\n",
    "\n",
    "l_text_columns = x_text_dfs.columns\n",
    "df_table1plus_final.loc[:, l_text_columns] = df_table1plus_final[l_text_columns].applymap(\n",
    "                                                    lambda x: 1 if x> 0 else 0\n",
    ")\n",
    "df_table1plus_3p_rev_month, l_consecute_man2p, l_consecute_man3p = get_3periods(\n",
    "                                        df_table1plus_final, l_high_lighted, l_text_columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f28fa663",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-10T01:33:26.753083Z",
     "iopub.status.busy": "2023-10-10T01:33:26.752847Z",
     "iopub.status.idle": "2023-10-10T01:33:29.048091Z",
     "shell.execute_reply": "2023-10-10T01:33:29.047585Z"
    }
   },
   "outputs": [],
   "source": [
    "file_meta = f\"{dir_path}/analysis/feature_groups_en_v3.csv\"\n",
    "df_meta_group = pd.read_csv(file_meta)\n",
    "df_meta_group.index = df_meta_group[\"item_id\"]\n",
    "rename_dict = df_meta_group[\"item_name_en\"].to_dict()\n",
    "\n",
    "output_dir = f\"{dir_path}/analysis/\"\n",
    "dict_obj = {\n",
    "    \"rename_dict\" : rename_dict,\n",
    "    \"l_consecute_man2p\" : l_consecute_man2p,\n",
    "    \"l_consecute_man3p\" : l_consecute_man3p\n",
    "}\n",
    "with open(f\"{output_dir}/man_info.pickle\", \"wb\") as f_out:\n",
    "    pickle.dump(dict_obj, f_out)\n",
    "\n",
    "df_table1plus_final_allmonths.to_parquet(f\"{output_dir}/tableOnePlusData-final_allmonths.parquet\")\n",
    "df_table1plus_final.to_parquet(f\"{output_dir}/tableOnePlusData-final.parquet\")\n",
    "df_table1plus_3p_rev_month.to_parquet(f\"{output_dir}/tableOnePlusData-final_3p.parquet\")\n",
    "df_meta_group.to_parquet(f\"{output_dir}/feature_groups_en_v3.parquet\")"
   ]
  }
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