{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Urgences - Image CC0 - pexels.com](img/pexels-pixabay-263402.jpg \"Urgences\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Challenge - [ED Lab Prediction]\n",
"_Nom à trouver_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Objectif"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ce notebook effectue le pre-processing des données. \n",
"Il exploite les données stockées dans la base sqlite, téléchargées à partir de `download_data.py` et les exporte dans un fichier csv à destination de l'entrainement de la data-visualisation l'algorithme."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chargement des données"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sqlite3\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Sqlite connection\n",
"conn = sqlite3.connect(\"./data/mimic-iv.sqlite\")\n",
"\n",
"# Classification des items de biologie\n",
"items = pd.read_csv(\"./config/lab_items.csv\").dropna()\n",
"items_list = items[\"item_id\"].astype(\"str\").tolist()\n",
"\n",
"# Classification ATC des médicaments\n",
"drugs_rules = pd.read_csv(\"./config/atc_items.csv\")\n",
"drugs_rules_list = drugs_rules[\"gsn\"].drop_duplicates().astype(\"str\").tolist()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Création d'un index pour accélérer les requêtes\n",
"conn.execute(\"CREATE INDEX IF NOT EXISTS biological_index ON labevents (stay_id, itemid)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stays dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"stays = pd.read_sql(f\"\"\"\n",
" SELECT \n",
" s.stay_id,\n",
" s.intime intime,\n",
" p.gender gender,\n",
" p.anchor_age age,\n",
" t.temperature,\n",
" t.heartrate,\n",
" t.resprate,\n",
" t.o2sat,\n",
" t.sbp,\n",
" t.dbp,\n",
" t.pain,\n",
" t.chiefcomplaint\n",
" FROM edstays s\n",
" LEFT JOIN patients p\n",
" ON p.subject_id = s.subject_id\n",
" LEFT Join triage t\n",
" ON t.stay_id = s.stay_id\n",
"\"\"\", conn)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Passage dans les 30 et 7 jours\n",
"\n",
"derniers_passages = pd.read_sql(f\"\"\"\n",
" SELECT DISTINCT\n",
" s1.stay_id,\n",
" CAST(MAX((julianday(s1.intime)-julianday(s2.intime))) <= 7 AS INT) last_7,\n",
" CAST(MAX((julianday(s1.intime)-julianday(s2.intime))) <= 30 AS INT) last_30\n",
" FROM edstays s1\n",
" INNER JOIN edstays s2\n",
" ON s1.subject_id = s2.subject_id\n",
" AND s1.stay_id != s2.stay_id\n",
" AND s1.intime >= s2.intime\n",
" WHERE (julianday(s1.intime)-julianday(s2.intime)) <= 30\n",
" GROUP BY s1.stay_id \n",
"\"\"\", conn)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Derniers diagnostic\n",
"from icdcodex import icd2vec, hierarchy\n",
"import numpy as np\n",
"\n",
"dernier_diag = pd.read_sql(f\"\"\"\n",
" SELECT \n",
" s1.stay_id,\n",
" d.icd_code,\n",
" d.icd_version,\n",
" COUNT(1) n\n",
" FROM edstays s1\n",
" INNER JOIN diagnosis d\n",
" ON d.subject_id = s1.subject_id\n",
" INNER JOIN edstays s2\n",
" ON d.stay_id = s2.stay_id\n",
" WHERE \n",
" s1.intime >= s2.intime\n",
" AND s1.stay_id != s2.stay_id\n",
" GROUP BY \n",
" s1.stay_id,\n",
" d.icd_code,\n",
" d.icd_version\n",
"\"\"\", conn)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"embedder_icd9 = icd2vec.Icd2Vec(num_embedding_dimensions=10, workers=-1)\n",
"embedder_icd9.fit(*hierarchy.icd9())\n",
"\n",
"icd_9 = dernier_diag.query(\"icd_version == 9\")[\"icd_code\"]\n",
"\n",
"# Hotfix\n",
"icd_9 = icd_9.replace(\"E119\",\"E0119\")\n",
"icd_9 = icd_9[icd_9.isin(hierarchy.icd9()[1])].drop_duplicates()\n",
"\n",
"\n",
"icd_9_embedding = embedder_icd9.to_vec(icd_9)\n",
"\n",
"embedder_icd10 = icd2vec.Icd2Vec(num_embedding_dimensions=10, workers=-1)\n",
"embedder_icd10.fit(*hierarchy.icd10cm(version=\"2020\"))\n",
"\n",
"icd_10 = dernier_diag.query(\"icd_version == 10\")[\"icd_code\"]\n",
"icd_10 = icd_10.apply(lambda x: x[0:3]+\".\"+x[3:] if len(x) > 3 else x)\n",
"icd_10 = icd_10[icd_10.isin(hierarchy.icd10cm(version=\"2020\")[1])].drop_duplicates()\n",
"icd_10_embedding = embedder_icd10.to_vec(icd_10)"
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {},
"outputs": [],
"source": [
"icd_series = pd.concat([\n",
" \"ICD9_\"+icd_9,\n",
" \"ICD10_\"+icd_10\n",
"]).reset_index(drop=True)\n",
"\n",
"dernier_diag_list = dernier_diag.assign(\n",
" icd_str = lambda x: \"ICD\"+x[\"icd_version\"].astype(\"str\")+\"_\"+x[\"icd_code\"],\n",
")[\"icd_str\"]\n",
"\n",
"icd_embeddings_matrix = np.concatenate([\n",
" icd_9_embedding,\n",
" icd_10_embedding\n",
"], axis=0)\n",
"\n",
"icd_to_idx = icd_series.reset_index().set_index(\"icd_code\").join(\n",
" dernier_diag_list.drop_duplicates().reset_index().set_index(\"icd_str\").drop(columns=\"index\"),\n",
" how=\"right\"\n",
").fillna(pd.NA).astype(pd.Int64Dtype())[\"index\"].to_dict()\n",
"\n",
"dernier_diag[\"icd_idx\"] = dernier_diag_list.apply(lambda x: icd_to_idx[x])\n",
"dernier_diag_idx = dernier_diag.dropna().groupby(\"stay_id\")[\"icd_idx\"].agg(lambda x: x.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 324,
"metadata": {},
"outputs": [],
"source": [
"from torch.nn import Embedding\n",
"from torch.nn.utils.rnn import pad_sequence\n",
"import torch\n",
"\n",
"icd_embeddings_matrix_with_pad = np.concatenate([\n",
" icd_embeddings_matrix,\n",
" np.zeros((1, icd_embeddings_matrix.shape[1]))\n",
"])\n",
"\n",
"torch_embedding = Embedding(\n",
" icd_embeddings_matrix_with_pad.shape[0],\n",
" embedding_dim=10,\n",
" _weight=torch.tensor(icd_embeddings_matrix_with_pad)\n",
")\n",
"torch_embedding.requires_grad = False"
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {},
"outputs": [],
"source": [
"dernier_diag_idx_tensor = pad_sequence([torch.tensor(x) for x in dernier_diag_idx.tolist()],\n",
" batch_first=True,\n",
" padding_value=icd_embeddings_matrix_with_pad.shape[0]-1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 361,
"metadata": {},
"outputs": [],
"source": [
"dernier_diag_idx_tensor_mask = (dernier_diag_idx_tensor != icd_embeddings_matrix_with_pad.shape[0]-1).unsqueeze(2)*1\n",
"dernier_diag_idx_tensor_embeddings = torch_embedding(dernier_diag_idx_tensor).sum(axis=1)/(dernier_diag_idx_tensor_mask.sum(axis=1)+1e-8)\n",
"dernier_diag_idx_tensor_embeddings = dernier_diag_idx_tensor_embeddings.detach().numpy()"
]
},
{
"cell_type": "code",
"execution_count": 371,
"metadata": {},
"outputs": [],
"source": [
"stays = stays.join(\n",
" pd.DataFrame(dernier_diag_idx_tensor_embeddings, index=dernier_diag_idx.index, columns=[\"diag_\"+str(x) for x in range(10)]),\n",
" on = \"stay_id\",\n",
" how=\"left\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"stays = stays \\\n",
" .join(derniers_passages.set_index(\"stay_id\"), on=\"stay_id\")\n",
"stays[\"last_7\"] = stays[\"last_7\"].fillna(0)\n",
"stays[\"last_30\"] = stays[\"last_30\"].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"stays[\"intime\"] = pd.to_datetime(stays[\"intime\"])\n",
"stays[\"gender\"] = stays[\"gender\"].astype(\"string\") # Pas de valeurs manquantes en gender\n",
"stays[\"chiefcomplaint\"] = stays[\"chiefcomplaint\"].fillna(\"\").astype(\"string\") # ¨Chiefcomplaint manquant = chiefcomplaint vide"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"drugs = pd.read_sql(f\"\"\"\n",
" SELECT stay_id, gsn, etccode, 1 n\n",
" FROM medrecon\n",
" WHERE gsn IN ({','.join(drugs_rules_list)})\n",
"\"\"\", conn)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Liste des codes ATC pour chaque séjour\n",
"atc_stays = pd.merge(\n",
" drugs,\n",
" drugs_rules,\n",
" left_on=\"gsn\",\n",
" right_on=\"gsn\"\n",
").groupby([\"stay_id\",\"atc\"])[\"n\"].sum() \\\n",
" .reset_index()\n",
"\n",
"atc_stays[\"atc_2\"] = atc_stays[\"atc\"].str.slice(0, 3)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Considérons 2 niveaux de granularité\n",
"## Le code ATC complet (Anatomique, Thérapeutique et Pharmacologique), ATC IV\n",
"\n",
"atc_stays_pivoted_4 = pd.pivot_table(\n",
" atc_stays[[\"stay_id\",\"atc\", \"n\"]],\n",
" columns=[\"atc\"],\n",
" index=[\"stay_id\"],\n",
" values=\"n\"\n",
").fillna(0).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"## Le code ATC 2 (Anatomique et Thérapeutique)\n",
"\n",
"atc_stays_pivoted_2 = pd.pivot_table(\n",
" atc_stays[[\"stay_id\",\"atc_2\", \"n\"]] \\\n",
" .groupby([\"stay_id\",\"atc_2\"])[\"n\"].sum() \\\n",
" .reset_index() \\\n",
" .rename(columns={\"atc_2\":\"atc\"}),\n",
" columns=[\"atc\"],\n",
" index=[\"stay_id\"],\n",
" values=\"n\"\n",
").fillna(0).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"## Les codes ETC\n",
"\n",
"etc_pivoted = pd.pivot_table(\n",
" drugs[[\"stay_id\",\"etccode\", \"n\"]].dropna() \\\n",
" .assign(etccode = lambda x: x[\"etccode\"].astype(\"int\").astype(\"str\")) \\\n",
" .groupby([\"stay_id\",\"etccode\"])[\"n\"].sum() \\\n",
" .reset_index() \\\n",
" .rename(columns={\"etccode\":\"atc\"}),\n",
" columns=[\"atc\"],\n",
" index=[\"stay_id\"],\n",
" values=\"n\"\n",
").fillna(0).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"stays_atc_4 = pd.merge(\n",
" stays,\n",
" atc_stays_pivoted_4,\n",
" left_on=\"stay_id\",\n",
" right_on=\"stay_id\",\n",
" how=\"left\"\n",
")\n",
"\n",
"stays_atc_2 = pd.merge(\n",
" stays,\n",
" atc_stays_pivoted_2,\n",
" left_on=\"stay_id\",\n",
" right_on=\"stay_id\",\n",
" how=\"left\"\n",
")\n",
"\n",
"stays_etc = pd.merge(\n",
" stays,\n",
" etc_pivoted,\n",
" left_on=\"stay_id\",\n",
" right_on=\"stay_id\",\n",
" how=\"left\"\n",
")\n",
"\n",
"stays_atc_4[atc_stays_pivoted_4.columns[1:]] = stays_atc_4[atc_stays_pivoted_4.columns[1:]].fillna(0)\n",
"stays_atc_2[atc_stays_pivoted_2.columns[1:]] = stays_atc_2[atc_stays_pivoted_2.columns[1:]].fillna(0)\n",
"stays_etc[etc_pivoted.columns[1:]] = stays_etc[etc_pivoted.columns[1:]].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Ecriture du featues dataset\n",
"# On écrit en parquet pour optimiser le stockage et les temps d'io\n",
"\n",
"stays_atc_2.sort_values(\"stay_id\").reset_index(drop=True).to_parquet(\"./data/features_atc2.parquet\", engine=\"pyarrow\", index=False)\n",
"stays_atc_4.sort_values(\"stay_id\").reset_index(drop=True).to_parquet(\"./data/features_atc4.parquet\", engine=\"pyarrow\", index=False)\n",
"stays_etc.sort_values(\"stay_id\").reset_index(drop=True).to_parquet(\"./data/features_etc.parquet\", engine=\"pyarrow\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lab dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m labs \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_sql(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m SELECT \u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m le.stay_id,\u001b[39m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m le.itemid item_id\u001b[39m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m FROM labevents le\u001b[39m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m WHERE le.itemid IN (\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(items_list)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m GROUP BY\u001b[39m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m le.stay_id,\u001b[39m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;124m le.itemid\u001b[39m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m, conn)\n",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"labs = pd.read_sql(f\"\"\"\n",
" SELECT \n",
" le.stay_id,\n",
" le.itemid item_id\n",
" FROM labevents le\n",
" WHERE le.itemid IN ('{\"','\".join(items_list)}')\n",
" GROUP BY\n",
" le.stay_id,\n",
" le.itemid\n",
"\"\"\", conn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"labs_deduplicate = pd.merge(\n",
" items[[\"item_id\",\"3\"]].rename(columns={\"3\":\"label\"}),\n",
" labs,\n",
" left_on=\"item_id\",\n",
" right_on=\"item_id\"\n",
") \\\n",
" .drop_duplicates([\"stay_id\", \"label\"])[[\"stay_id\",\"label\"]] \\\n",
" .reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"labs_deduplicate_pivot = pd.pivot_table(\n",
" labs_deduplicate.assign(value=1),\n",
" index=[\"stay_id\"],\n",
" columns=[\"label\"],\n",
" values=\"value\"\n",
").fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"labs_deduplicate_pivot_final = labs_deduplicate_pivot.join(\n",
" stays[[\"stay_id\"]].set_index(\"stay_id\"),\n",
" how=\"right\"\n",
").fillna(0).astype(\"int8\").reset_index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"labs_deduplicate_pivot_final.sort_values(\"stay_id\").reset_index(drop=True).to_parquet(\"./data/labels.parquet\", index=False)"
]
}
],
"metadata": {
"interpreter": {
"hash": "c304935560631f5a20c1bdabb506947800ccd82d813704000c078f0735b9b818"
},
"kernelspec": {
"display_name": "R",
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"language_info": {
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"file_extension": ".py",
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