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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EDA\n",
"This notebook is used to perform exploratory data analysis on the data.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/mnt/d/Google Drive/projects/medical_txt_parser/src/notebooks\n",
"/mnt/d/Google Drive/projects/medical_txt_parser/src\n",
"/mnt/d/Google Drive/projects/medical_txt_parser\n"
]
}
],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n",
"\n",
"path = %pwd\n",
"while \"src\" in path:\n",
" %cd ..\n",
" path = %pwd\n",
"\n",
"import glob\n",
"import pandas as pd\n",
"import os\n",
"from tqdm.notebook import tqdm\n",
"from pprint import pprint\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from src.utils.parse_data import parse_ast, parse_concept, parse_relation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"train_data_path = \"data/train\"\n",
"val_data_path = \"data/val\"\n",
"ast_folder_name = \"ast\"\n",
"concept_folder_name = \"concept\"\n",
"rel_folder_name = \"rel\"\n",
"txt_folder_name = \"txt\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0a2609234fd24eb8a35993fca49a0fc2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/170 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>filename</th>\n",
" <th>concept</th>\n",
" <th>ast</th>\n",
" <th>rel</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>018636330 DH\\n5425710\\n123524\\n0144918\\n6/2/20...</td>\n",
" <td>018636330_DH</td>\n",
" <td>{'concept_text': ['a workup', 'pain', 'microsc...</td>\n",
" <td>{'concept_text': ['pain', 'hyperlipidemia', 'h...</td>\n",
" <td>{'concept_text_1': ['po pain medications', 'a ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>026350193 RWH\\n7093319\\n549304\\n8417371\\n6/5/2...</td>\n",
" <td>026350193_RWH</td>\n",
" <td>{'concept_text': ['flexeril', 'constipation', ...</td>\n",
" <td>{'concept_text': ['constipation', 'left should...</td>\n",
" <td>{'concept_text_1': [], 'start_line_1': [], 'st...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>037945397 RWH\\n2690633\\n194867\\n151887\\n10/17/...</td>\n",
" <td>037945397_RWH</td>\n",
" <td>{'concept_text': ['ivf', 'near syncope', 'recu...</td>\n",
" <td>{'concept_text': ['near syncope', 'recurrent d...</td>\n",
" <td>{'concept_text_1': [], 'start_line_1': [], 'st...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>044687343 ELMVH\\n01719921\\n1626859\\n3/13/2006 ...</td>\n",
" <td>044687343_ELMVH</td>\n",
" <td>{'concept_text': ['lisinopril pump', 'bipap', ...</td>\n",
" <td>{'concept_text': ['copd', 'nad', 'fatigue', 'g...</td>\n",
" <td>{'concept_text_1': ['bipap', 'fatigue', 'ekg',...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>060376519 DH\\n0649031\\n323495\\n3838556\\n4/5/20...</td>\n",
" <td>060376519_DH</td>\n",
" <td>{'concept_text': ['dizziness', 'benign positio...</td>\n",
" <td>{'concept_text': ['dizziness', 'benign positio...</td>\n",
" <td>{'concept_text_1': ['fever'], 'start_line_1': ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text filename \\\n",
"0 018636330 DH\\n5425710\\n123524\\n0144918\\n6/2/20... 018636330_DH \n",
"1 026350193 RWH\\n7093319\\n549304\\n8417371\\n6/5/2... 026350193_RWH \n",
"2 037945397 RWH\\n2690633\\n194867\\n151887\\n10/17/... 037945397_RWH \n",
"3 044687343 ELMVH\\n01719921\\n1626859\\n3/13/2006 ... 044687343_ELMVH \n",
"4 060376519 DH\\n0649031\\n323495\\n3838556\\n4/5/20... 060376519_DH \n",
"\n",
" concept \\\n",
"0 {'concept_text': ['a workup', 'pain', 'microsc... \n",
"1 {'concept_text': ['flexeril', 'constipation', ... \n",
"2 {'concept_text': ['ivf', 'near syncope', 'recu... \n",
"3 {'concept_text': ['lisinopril pump', 'bipap', ... \n",
"4 {'concept_text': ['dizziness', 'benign positio... \n",
"\n",
" ast \\\n",
"0 {'concept_text': ['pain', 'hyperlipidemia', 'h... \n",
"1 {'concept_text': ['constipation', 'left should... \n",
"2 {'concept_text': ['near syncope', 'recurrent d... \n",
"3 {'concept_text': ['copd', 'nad', 'fatigue', 'g... \n",
"4 {'concept_text': ['dizziness', 'benign positio... \n",
"\n",
" rel \n",
"0 {'concept_text_1': ['po pain medications', 'a ... \n",
"1 {'concept_text_1': [], 'start_line_1': [], 'st... \n",
"2 {'concept_text_1': [], 'start_line_1': [], 'st... \n",
"3 {'concept_text_1': ['bipap', 'fatigue', 'ekg',... \n",
"4 {'concept_text_1': ['fever'], 'start_line_1': ... "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_files = glob.glob(train_data_path + os.sep + txt_folder_name + os.sep + \"*.txt\")\n",
"filename = \"\"\n",
"df = pd.DataFrame()\n",
"for file in tqdm(text_files):\n",
" with open(file, 'r') as f:\n",
" text = f.read()\n",
" filename = file.split(\"/\")[-1].split(\".\")[0]\n",
" ast = parse_ast(train_data_path + os.sep + ast_folder_name + os.sep + filename + \".ast\")\n",
" concept = parse_concept(train_data_path + os.sep + concept_folder_name + os.sep + filename + \".con\")\n",
" rel = parse_relation(train_data_path + os.sep + rel_folder_name + os.sep + filename + \".rel\")\n",
" \n",
" df = df.append(pd.DataFrame({\"text\": [text], \"filename\": [filename] , \"concept\": [concept], \"ast\": [ast], \"rel\": [rel]}), ignore_index=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"a = df[[\"text\", \"filename\"]].set_index(\"filename\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'018636330 DH\\n5425710\\n123524\\n0144918\\n6/2/2005 12:00:00 AM\\nDischarge Summary\\nSigned\\nDIS\\nReport Status :\\nSigned\\nDISCHARGE SUMMARY\\nNAME :\\nKOTE , OA\\nUNIT NUMBER :\\n509-22-30\\nADMISSION DATE :\\n06/02/2005\\nDISCHARGE DATE :\\n06/05/2005\\nPRINCIPAL DIAGNOSIS :\\nC5-6 disc herniation with cord compression and myelopathy .\\nPRINCIPAL PROCEDURE :\\nMicroscopic anterior cervical diskectomy at C5-6 and fusion .\\nHISTORY OF PRESENT ILLNESS :\\nThe patient is a 63-year-old female with a three-year history of bilateral hand numbness and occasional weakness .\\nWithin the past year , these symptoms have progressively gotten worse , to encompass also her feet .\\nShe had a workup by her neurologist and an MRI revealed a C5-6 disc herniation with cord compression and a T2 signal change at that level .\\nPAST MEDICAL HISTORY :\\nSignificant for hypertension , hyperlipidemia .\\nMEDICATIONS ON ADMISSION :\\nLipitor , Flexeril , hydrochlorothiazide and Norvasc .\\nALLERGIES :\\nShe has no known drug allergy .\\nSOCIAL HISTORY :\\nShe smokes one pack per day x45 years .\\nShe occasionally drinks alcohol .\\nPHYSICAL EXAMINATION :\\nShe had 5/5 strength in bilateral upper and lower extremities .\\nShe had a Hoffman 's sign greater on the right than the left and she had 10 beats of clonus in the right foot and 3-5 beats in the left foot .\\nShe had hyperreflexia in both the bilateral upper and lower extremities .\\nHOSPITAL COURSE :\\nThe patient tolerated a C5-6 ACDF by Dr. Miezetri Gach quite well .\\nShe had a postoperative CT scan that revealed partial decompression of the spinal canal and good placement of her hardware .\\nImmediately postop , her exam only improved slightly in her hyperreflexia .\\nShe was ambulating by postoperative day number two .\\nShe tolerated a regular diet .\\nHer pain was under good control with PO pain medications and she was deemed suitable for discharge .\\nDISCHARGE ORDERS :\\nThe patient was asked to call Dr. Miezetri Gach 's office for a follow-up appointment and wound check .\\nShe is asked to call with any fevers , chills , increasing weakness or numbness or any bowel and bladder disruption .\\nDISCHARGE MEDICATIONS :\\nShe was discharged on the following medications .\\n1. Colace , 100 mg PO bid .\\n2. Zantac , 150 mg PO bid .\\n3. Percocet , 5/325 , 1-2 tabs PO q4-6h prn pain .\\n4. Lipitor , 10 mg PO daily .\\n5. Hydrochlorothiazide , 25 mg PO daily .\\n6. Norvasc , 5 mg PO daily .\\nLALIND KOTE , M.D.\\nDICTATING FOR :\\nElectronically Signed MIEZETRI NIMIRY POP , M.D. 08/01/2005 18:50\\n_____________________________ MIEZETRI NIMIRY POP , M.D.\\nTR :\\nqg\\nDD :\\n07/30/2005\\nTD :\\n07/31/2005 9:43 A 123524\\ncc :\\nMIEZETRI NIMIRY POP , M.D.\\n'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.loc[\"018636330_DH\"][\"text\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['concept_text', 'start_line', 'start_word_number', 'end_line', 'end_word_number', 'concept_type'])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"concept\"][0].keys()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
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" <th>start_line</th>\n",
" <th>start_word_number</th>\n",
" <th>end_line</th>\n",
" <th>end_word_number</th>\n",
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" <td>pain</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" <td>problem</td>\n",
" <td>hypothetical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>018636330_DH</td>\n",
" <td>hyperlipidemia</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>018636330_DH</td>\n",
" <td>her pain</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>018636330_DH</td>\n",
" <td>cord compression</td>\n",
" <td>27</td>\n",
" <td>16</td>\n",
" <td>27</td>\n",
" <td>17</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>018636330_DH</td>\n",
" <td>chills</td>\n",
" <td>50</td>\n",
" <td>9</td>\n",
" <td>50</td>\n",
" <td>9</td>\n",
" <td>problem</td>\n",
" <td>hypothetical</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line start_word_number end_line \\\n",
"0 018636330_DH pain 55 10 55 \n",
"1 018636330_DH hyperlipidemia 29 4 29 \n",
"2 018636330_DH her pain 47 0 47 \n",
"3 018636330_DH cord compression 27 16 27 \n",
"4 018636330_DH chills 50 9 50 \n",
"\n",
" end_word_number concept_type assertion_type \n",
"0 10 problem hypothetical \n",
"1 4 problem present \n",
"2 1 problem present \n",
"3 17 problem present \n",
"4 9 problem hypothetical "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ast_df = pd.DataFrame(columns=[\"filename\"]+list(ast.keys()))\n",
"for i, file in df.iterrows():\n",
" ast_dict = file[\"ast\"]\n",
" tmp = pd.DataFrame(ast_dict)\n",
" tmp[\"filename\"] = file[\"filename\"]\n",
" ast_df = ast_df.append(tmp, ignore_index=True)\n",
"ast_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>concept_text</th>\n",
" <th>start_line</th>\n",
" <th>start_word_number</th>\n",
" <th>end_line</th>\n",
" <th>end_word_number</th>\n",
" <th>concept_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>018636330_DH</td>\n",
" <td>a workup</td>\n",
" <td>27</td>\n",
" <td>2</td>\n",
" <td>27</td>\n",
" <td>3</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>018636330_DH</td>\n",
" <td>pain</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" <td>problem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>018636330_DH</td>\n",
" <td>microscopic anterior cervical diskectomy at c5-6</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>5</td>\n",
" <td>treatment</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>018636330_DH</td>\n",
" <td>hyperlipidemia</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>problem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>018636330_DH</td>\n",
" <td>po pain medications</td>\n",
" <td>47</td>\n",
" <td>7</td>\n",
" <td>47</td>\n",
" <td>9</td>\n",
" <td>treatment</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line \\\n",
"0 018636330_DH a workup 27 \n",
"1 018636330_DH pain 55 \n",
"2 018636330_DH microscopic anterior cervical diskectomy at c5-6 23 \n",
"3 018636330_DH hyperlipidemia 29 \n",
"4 018636330_DH po pain medications 47 \n",
"\n",
" start_word_number end_line end_word_number concept_type \n",
"0 2 27 3 test \n",
"1 10 55 10 problem \n",
"2 0 23 5 treatment \n",
"3 4 29 4 problem \n",
"4 7 47 9 treatment "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"concept_df = pd.DataFrame(columns=[ \"filename\"]+list(concept.keys()))\n",
"for i, file in df.iterrows():\n",
" concept_dict = file[\"concept\"]\n",
" tmp = pd.DataFrame(concept_dict)\n",
" tmp[\"filename\"] = file[\"filename\"]\n",
" concept_df = concept_df.append(tmp, ignore_index=True)\n",
"concept_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"elements in concept_prob_df: 7073\n",
"elements in ast_df: 7073\n"
]
}
],
"source": [
"concept_prob_df = concept_df[concept_df[\"concept_type\"] == \"problem\"]\n",
"print(\"elements in concept_prob_df: \", len(concept_prob_df))\n",
"print(\"elements in ast_df: \", len(ast_df))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"concept_prob_df['concept_text'].reset_index(drop=True).equals(ast_df['concept_text'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This means that the `problem` type is totally encoded in the ast data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Better data representation"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>concept_text</th>\n",
" <th>start_line</th>\n",
" <th>start_word_number</th>\n",
" <th>end_line</th>\n",
" <th>end_word_number</th>\n",
" <th>ast_con_label</th>\n",
" <th>filename</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16425</th>\n",
" <td>diaphoresis</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>present</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16426</th>\n",
" <td>ectopy</td>\n",
" <td>68.0</td>\n",
" <td>11.0</td>\n",
" <td>68.0</td>\n",
" <td>11.0</td>\n",
" <td>absent</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16427</th>\n",
" <td>further pain</td>\n",
" <td>24.0</td>\n",
" <td>12.0</td>\n",
" <td>24.0</td>\n",
" <td>13.0</td>\n",
" <td>absent</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16428</th>\n",
" <td>nontender</td>\n",
" <td>53.0</td>\n",
" <td>7.0</td>\n",
" <td>53.0</td>\n",
" <td>7.0</td>\n",
" <td>absent</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16429</th>\n",
" <td>jvd</td>\n",
" <td>47.0</td>\n",
" <td>1.0</td>\n",
" <td>47.0</td>\n",
" <td>1.0</td>\n",
" <td>absent</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16520</th>\n",
" <td>hydrochlorothiazide</td>\n",
" <td>99.0</td>\n",
" <td>30.0</td>\n",
" <td>99.0</td>\n",
" <td>30.0</td>\n",
" <td>treatment</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16521</th>\n",
" <td>his electrolytes</td>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>80.0</td>\n",
" <td>1.0</td>\n",
" <td>test</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16522</th>\n",
" <td>nitroglycerin</td>\n",
" <td>59.0</td>\n",
" <td>42.0</td>\n",
" <td>59.0</td>\n",
" <td>42.0</td>\n",
" <td>treatment</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16523</th>\n",
" <td>auscultation</td>\n",
" <td>52.0</td>\n",
" <td>5.0</td>\n",
" <td>52.0</td>\n",
" <td>5.0</td>\n",
" <td>test</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16524</th>\n",
" <td>nitroglycerin</td>\n",
" <td>20.0</td>\n",
" <td>12.0</td>\n",
" <td>20.0</td>\n",
" <td>12.0</td>\n",
" <td>treatment</td>\n",
" <td>record-84</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" concept_text start_line start_word_number end_line \\\n",
"16425 diaphoresis 14.0 15.0 14.0 \n",
"16426 ectopy 68.0 11.0 68.0 \n",
"16427 further pain 24.0 12.0 24.0 \n",
"16428 nontender 53.0 7.0 53.0 \n",
"16429 jvd 47.0 1.0 47.0 \n",
"... ... ... ... ... \n",
"16520 hydrochlorothiazide 99.0 30.0 99.0 \n",
"16521 his electrolytes 80.0 0.0 80.0 \n",
"16522 nitroglycerin 59.0 42.0 59.0 \n",
"16523 auscultation 52.0 5.0 52.0 \n",
"16524 nitroglycerin 20.0 12.0 20.0 \n",
"\n",
" end_word_number ast_con_label filename \n",
"16425 15.0 present record-84 \n",
"16426 11.0 absent record-84 \n",
"16427 13.0 absent record-84 \n",
"16428 7.0 absent record-84 \n",
"16429 1.0 absent record-84 \n",
"... ... ... ... \n",
"16520 30.0 treatment record-84 \n",
"16521 1.0 test record-84 \n",
"16522 42.0 treatment record-84 \n",
"16523 5.0 test record-84 \n",
"16524 12.0 treatment record-84 \n",
"\n",
"[100 rows x 7 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ast_concept_df = pd.DataFrame(columns=[\"filename\"]+list(ast.keys()))\n",
"ast_concept_df = pd.DataFrame()\n",
"for i, file in df.iterrows():\n",
" ast_dict = file[\"ast\"]\n",
" concept_dict = file[\"concept\"]\n",
" tmp_ast = pd.DataFrame(ast_dict)\n",
" tmp_ast = tmp_ast.drop(columns=[\"concept_type\"])\n",
" tmp_ast = tmp_ast.rename(columns={\"assertion_type\": \"ast_con_label\"})\n",
"\n",
" #Only concepts with not \"problem\"\n",
" tmp_concept = pd.DataFrame(concept_dict)\n",
" tmp_concept = tmp_concept[tmp_concept[\"concept_type\"] != \"problem\"]\n",
" tmp_concept = tmp_concept.rename(columns={\"concept_type\": \"ast_con_label\"})\n",
" \n",
" tmp_ast[\"filename\"] = file[\"filename\"]\n",
" tmp_concept[\"filename\"] = file[\"filename\"]\n",
" ast_concept_df = ast_concept_df.append(tmp_ast, ignore_index=True)\n",
" ast_concept_df = ast_concept_df.append(tmp_concept, ignore_index=True)\n",
"ast_concept_df.tail(100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Concept Analysis"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>concept_text</th>\n",
" <th>start_line</th>\n",
" <th>start_word_number</th>\n",
" <th>end_line</th>\n",
" <th>end_word_number</th>\n",
" <th>concept_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>018636330_DH</td>\n",
" <td>a workup</td>\n",
" <td>27</td>\n",
" <td>2</td>\n",
" <td>27</td>\n",
" <td>3</td>\n",
" <td>test</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>018636330_DH</td>\n",
" <td>pain</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" <td>problem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>018636330_DH</td>\n",
" <td>microscopic anterior cervical diskectomy at c5-6</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>5</td>\n",
" <td>treatment</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>018636330_DH</td>\n",
" <td>hyperlipidemia</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>problem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>018636330_DH</td>\n",
" <td>po pain medications</td>\n",
" <td>47</td>\n",
" <td>7</td>\n",
" <td>47</td>\n",
" <td>9</td>\n",
" <td>treatment</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line \\\n",
"0 018636330_DH a workup 27 \n",
"1 018636330_DH pain 55 \n",
"2 018636330_DH microscopic anterior cervical diskectomy at c5-6 23 \n",
"3 018636330_DH hyperlipidemia 29 \n",
"4 018636330_DH po pain medications 47 \n",
"\n",
" start_word_number end_line end_word_number concept_type \n",
"0 2 27 3 test \n",
"1 10 55 10 problem \n",
"2 0 23 5 treatment \n",
"3 4 29 4 problem \n",
"4 7 47 9 treatment "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"concept_df = pd.DataFrame(columns=[ \"filename\"]+list(concept.keys()))\n",
"for i, file in df.iterrows():\n",
" concept_dict = file[\"concept\"]\n",
" tmp = pd.DataFrame(concept_dict)\n",
" tmp[\"filename\"] = file[\"filename\"]\n",
" concept_df = concept_df.append(tmp, ignore_index=True)\n",
"concept_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some annotations are duplicated in the data we have. So we need to drop them"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"filename concept_text start_line start_word_number end_line end_word_number concept_type\n",
"245096078 kayciel 131 70 131 70 treatment 2\n",
"627258104 cultures 83 0 83 0 test 2\n",
"555509347_PUMC multivitamins 62 0 62 0 treatment 2\n",
"523704694 proctofoam 36 35 36 35 treatment 2\n",
"641557794_WGH papillary carcinoma 50 0 50 1 problem 2\n",
"dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"concept_df.value_counts().head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"filename concept_text start_line start_word_number end_line end_word_number concept_type\n",
"018636330_DH 10 beats of clonus 39 16 39 19 problem 1\n",
"record-25 placement of 8 caucasian nephrostomy catheter 21 9 21 14 treatment 1\n",
" initial work-up 87 8 87 9 test 1\n",
" inr 42 0 42 0 test 1\n",
" intravenous albumin 79 0 79 1 treatment 1\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"concept_df = concept_df.drop_duplicates()\n",
"concept_df.value_counts().head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Feature `concept_type`"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Number of Concepts per File')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 720x2520 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = [10, 35]\n",
"concept_df[[\"concept_type\", \"filename\"]].groupby(\"filename\").count().sort_values(by=\"concept_type\", ascending=True).plot(kind=\"barh\")\n",
"plt.title(\"Number of Concepts per File\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" </tr>\n",
" <tr>\n",
" <th>concept_type</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>problem</th>\n",
" <td>7072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>test</th>\n",
" <td>4607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>treatment</th>\n",
" <td>4841</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename\n",
"concept_type \n",
"problem 7072\n",
"test 4607\n",
"treatment 4841"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1800x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# number of concept types\n",
"concept_df[[\"concept_type\", \"filename\"]].groupby(\"concept_type\").count().plot(kind=\"barh\")\n",
"concept_df[[\"concept_type\", \"filename\"]].groupby(\"concept_type\").count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assertion Analysis"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>concept_text</th>\n",
" <th>start_line</th>\n",
" <th>start_word_number</th>\n",
" <th>end_line</th>\n",
" <th>end_word_number</th>\n",
" <th>concept_type</th>\n",
" <th>assertion_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>018636330_DH</td>\n",
" <td>pain</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" <td>55</td>\n",
" <td>10</td>\n",
" <td>problem</td>\n",
" <td>hypothetical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>018636330_DH</td>\n",
" <td>hyperlipidemia</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>018636330_DH</td>\n",
" <td>her pain</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>018636330_DH</td>\n",
" <td>cord compression</td>\n",
" <td>27</td>\n",
" <td>16</td>\n",
" <td>27</td>\n",
" <td>17</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>018636330_DH</td>\n",
" <td>chills</td>\n",
" <td>50</td>\n",
" <td>9</td>\n",
" <td>50</td>\n",
" <td>9</td>\n",
" <td>problem</td>\n",
" <td>hypothetical</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line start_word_number end_line \\\n",
"0 018636330_DH pain 55 10 55 \n",
"1 018636330_DH hyperlipidemia 29 4 29 \n",
"2 018636330_DH her pain 47 0 47 \n",
"3 018636330_DH cord compression 27 16 27 \n",
"4 018636330_DH chills 50 9 50 \n",
"\n",
" end_word_number concept_type assertion_type \n",
"0 10 problem hypothetical \n",
"1 4 problem present \n",
"2 1 problem present \n",
"3 17 problem present \n",
"4 9 problem hypothetical "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertion_df = pd.DataFrame(columns=[ \"filename\"]+list(ast.keys()))\n",
"for i, file in df.iterrows():\n",
" assertion_dict = file[\"ast\"]\n",
" tmp = pd.DataFrame(assertion_dict)\n",
" tmp[\"filename\"] = file[\"filename\"]\n",
" assertion_df = assertion_df.append(tmp, ignore_index=True)\n",
"assertion_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" </tr>\n",
" <tr>\n",
" <th>assertion_type</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>conditional</th>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>associated_with_someone_else</th>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>possible</th>\n",
" <td>309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hypothetical</th>\n",
" <td>382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>absent</th>\n",
" <td>1596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>present</th>\n",
" <td>4624</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename\n",
"assertion_type \n",
"conditional 73\n",
"associated_with_someone_else 89\n",
"possible 309\n",
"hypothetical 382\n",
"absent 1596\n",
"present 4624"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# count assertion types\n",
"assertion_df[[\"assertion_type\", \"filename\"]].groupby(\"assertion_type\").count().sort_values(by=\"filename\", ascending=True).plot(kind=\"barh\")\n",
"assertion_df[[\"assertion_type\", \"filename\"]].groupby(\"assertion_type\").count().sort_values(by=\"filename\", ascending=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Present\n",
"problems associated with the patient can be present. This is the default category for medical problems and it contains that do not fit the definition of any of the other assertion category."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>concept_text</th>\n",
" <th>start_line</th>\n",
" <th>start_word_number</th>\n",
" <th>end_line</th>\n",
" <th>end_word_number</th>\n",
" <th>concept_type</th>\n",
" <th>assertion_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>018636330_DH</td>\n",
" <td>hyperlipidemia</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>29</td>\n",
" <td>4</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>018636330_DH</td>\n",
" <td>her pain</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>018636330_DH</td>\n",
" <td>cord compression</td>\n",
" <td>27</td>\n",
" <td>16</td>\n",
" <td>27</td>\n",
" <td>17</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>018636330_DH</td>\n",
" <td>hyperreflexia</td>\n",
" <td>40</td>\n",
" <td>2</td>\n",
" <td>40</td>\n",
" <td>2</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>018636330_DH</td>\n",
" <td>partial decompression of the spinal canal</td>\n",
" <td>43</td>\n",
" <td>8</td>\n",
" <td>43</td>\n",
" <td>13</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7066</th>\n",
" <td>record-84</td>\n",
" <td>minimal ooze</td>\n",
" <td>55</td>\n",
" <td>5</td>\n",
" <td>55</td>\n",
" <td>6</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7067</th>\n",
" <td>record-84</td>\n",
" <td>10/10 substernal chest pain</td>\n",
" <td>12</td>\n",
" <td>37</td>\n",
" <td>12</td>\n",
" <td>40</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7068</th>\n",
" <td>record-84</td>\n",
" <td>st elevations in v1-v3</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" <td>17</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7069</th>\n",
" <td>record-84</td>\n",
" <td>very tense</td>\n",
" <td>43</td>\n",
" <td>5</td>\n",
" <td>43</td>\n",
" <td>6</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7072</th>\n",
" <td>record-84</td>\n",
" <td>hypertension</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>problem</td>\n",
" <td>present</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4624 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line \\\n",
"1 018636330_DH hyperlipidemia 29 \n",
"2 018636330_DH her pain 47 \n",
"3 018636330_DH cord compression 27 \n",
"5 018636330_DH hyperreflexia 40 \n",
"6 018636330_DH partial decompression of the spinal canal 43 \n",
"... ... ... ... \n",
"7066 record-84 minimal ooze 55 \n",
"7067 record-84 10/10 substernal chest pain 12 \n",
"7068 record-84 st elevations in v1-v3 15 \n",
"7069 record-84 very tense 43 \n",
"7072 record-84 hypertension 12 \n",
"\n",
" start_word_number end_line end_word_number concept_type assertion_type \n",
"1 4 29 4 problem present \n",
"2 0 47 1 problem present \n",
"3 16 27 17 problem present \n",
"5 2 40 2 problem present \n",
"6 8 43 13 problem present \n",
"... ... ... ... ... ... \n",
"7066 5 55 6 problem present \n",
"7067 37 12 40 problem present \n",
"7068 14 15 17 problem present \n",
"7069 5 43 6 problem present \n",
"7072 30 12 30 problem present \n",
"\n",
"[4624 rows x 8 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# examples of as = \"present\"\n",
"assertion_df[assertion_df[\"assertion_type\"] == \"present\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Absent\n",
"the note asserts that the problem does not exist in the patient. This category also includes mentions where it is stated that the patient HAD a problem, but no longer does."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>concept_text</th>\n",
" <th>start_line</th>\n",
" <th>start_word_number</th>\n",
" <th>end_line</th>\n",
" <th>end_word_number</th>\n",
" <th>concept_type</th>\n",
" <th>assertion_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>018636330_DH</td>\n",
" <td>known drug allergy</td>\n",
" <td>33</td>\n",
" <td>3</td>\n",
" <td>33</td>\n",
" <td>5</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>044687343_ELMVH</td>\n",
" <td>nad</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>044687343_ELMVH</td>\n",
" <td>st changes</td>\n",
" <td>81</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>7</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>044687343_ELMVH</td>\n",
" <td>wheezes</td>\n",
" <td>80</td>\n",
" <td>19</td>\n",
" <td>80</td>\n",
" <td>19</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>044687343_ELMVH</td>\n",
" <td>uti</td>\n",
" <td>92</td>\n",
" <td>14</td>\n",
" <td>92</td>\n",
" <td>14</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7061</th>\n",
" <td>record-84</td>\n",
" <td>edema</td>\n",
" <td>54</td>\n",
" <td>8</td>\n",
" <td>54</td>\n",
" <td>8</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7062</th>\n",
" <td>record-84</td>\n",
" <td>vomiting</td>\n",
" <td>24</td>\n",
" <td>21</td>\n",
" <td>24</td>\n",
" <td>21</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7064</th>\n",
" <td>record-84</td>\n",
" <td>any chest pain</td>\n",
" <td>86</td>\n",
" <td>4</td>\n",
" <td>86</td>\n",
" <td>6</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7070</th>\n",
" <td>record-84</td>\n",
" <td>pnd</td>\n",
" <td>25</td>\n",
" <td>18</td>\n",
" <td>25</td>\n",
" <td>18</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7071</th>\n",
" <td>record-84</td>\n",
" <td>gallops</td>\n",
" <td>51</td>\n",
" <td>10</td>\n",
" <td>51</td>\n",
" <td>10</td>\n",
" <td>problem</td>\n",
" <td>absent</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1596 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line start_word_number \\\n",
"22 018636330_DH known drug allergy 33 3 \n",
"39 044687343_ELMVH nad 80 0 \n",
"42 044687343_ELMVH st changes 81 6 \n",
"48 044687343_ELMVH wheezes 80 19 \n",
"49 044687343_ELMVH uti 92 14 \n",
"... ... ... ... ... \n",
"7061 record-84 edema 54 8 \n",
"7062 record-84 vomiting 24 21 \n",
"7064 record-84 any chest pain 86 4 \n",
"7070 record-84 pnd 25 18 \n",
"7071 record-84 gallops 51 10 \n",
"\n",
" end_line end_word_number concept_type assertion_type \n",
"22 33 5 problem absent \n",
"39 80 0 problem absent \n",
"42 81 7 problem absent \n",
"48 80 19 problem absent \n",
"49 92 14 problem absent \n",
"... ... ... ... ... \n",
"7061 54 8 problem absent \n",
"7062 24 21 problem absent \n",
"7064 86 6 problem absent \n",
"7070 25 18 problem absent \n",
"7071 51 10 problem absent \n",
"\n",
"[1596 rows x 8 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertion_df[assertion_df[\"assertion_type\"] == \"absent\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Possible:\n",
"the note asserts that the patient may have a problem, but there is\n",
"uncertainty expressed in the note. Possible takes precedence over absent, so\n",
"terms like “probably not” or “unlikely” categorize problems as being possible\n",
"just as “probably” and “likely” do."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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" <th>93</th>\n",
" <td>060376519_DH</td>\n",
" <td>benign positional vertigo</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>35</td>\n",
" <td>2</td>\n",
" <td>problem</td>\n",
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" <th>98</th>\n",
" <td>060376519_DH</td>\n",
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" <td>35</td>\n",
" <td>4</td>\n",
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" <th>171</th>\n",
" <td>101407944_PUMC</td>\n",
" <td>glioblastoma</td>\n",
" <td>99</td>\n",
" <td>16</td>\n",
" <td>99</td>\n",
" <td>16</td>\n",
" <td>problem</td>\n",
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" <tr>\n",
" <th>181</th>\n",
" <td>101407944_PUMC</td>\n",
" <td>ependymal spread</td>\n",
" <td>100</td>\n",
" <td>13</td>\n",
" <td>100</td>\n",
" <td>14</td>\n",
" <td>problem</td>\n",
" <td>possible</td>\n",
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" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6978</th>\n",
" <td>record-83</td>\n",
" <td>gastritis</td>\n",
" <td>41</td>\n",
" <td>33</td>\n",
" <td>41</td>\n",
" <td>33</td>\n",
" <td>problem</td>\n",
" <td>possible</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6979</th>\n",
" <td>record-83</td>\n",
" <td>gastric ulcer</td>\n",
" <td>41</td>\n",
" <td>35</td>\n",
" <td>41</td>\n",
" <td>36</td>\n",
" <td>problem</td>\n",
" <td>possible</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6980</th>\n",
" <td>record-83</td>\n",
" <td>lower gi pathology</td>\n",
" <td>41</td>\n",
" <td>40</td>\n",
" <td>41</td>\n",
" <td>42</td>\n",
" <td>problem</td>\n",
" <td>possible</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6986</th>\n",
" <td>record-83</td>\n",
" <td>gastroenteritis</td>\n",
" <td>37</td>\n",
" <td>0</td>\n",
" <td>37</td>\n",
" <td>0</td>\n",
" <td>problem</td>\n",
" <td>possible</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6988</th>\n",
" <td>record-83</td>\n",
" <td>infection</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>problem</td>\n",
" <td>possible</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>309 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line start_word_number \\\n",
"93 060376519_DH benign positional vertigo 35 0 \n",
"98 060376519_DH labyrinthitis 35 4 \n",
"150 101407944_PUMC primary cns neoplasm 99 11 \n",
"171 101407944_PUMC glioblastoma 99 16 \n",
"181 101407944_PUMC ependymal spread 100 13 \n",
"... ... ... ... ... \n",
"6978 record-83 gastritis 41 33 \n",
"6979 record-83 gastric ulcer 41 35 \n",
"6980 record-83 lower gi pathology 41 40 \n",
"6986 record-83 gastroenteritis 37 0 \n",
"6988 record-83 infection 21 0 \n",
"\n",
" end_line end_word_number concept_type assertion_type \n",
"93 35 2 problem possible \n",
"98 35 4 problem possible \n",
"150 99 13 problem possible \n",
"171 99 16 problem possible \n",
"181 100 14 problem possible \n",
"... ... ... ... ... \n",
"6978 41 33 problem possible \n",
"6979 41 36 problem possible \n",
"6980 41 42 problem possible \n",
"6986 37 0 problem possible \n",
"6988 21 0 problem possible \n",
"\n",
"[309 rows x 8 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertion_df[assertion_df[\"assertion_type\"] == \"possible\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Conditional:\n",
"the mention of the medical problem asserts that the patient\n",
"experiences the problem only under certain conditions. Allergies can fall into\n",
"this category."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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" <th>345</th>\n",
" <td>143748600_SC</td>\n",
" <td>episodes of atypical cp x 1 week</td>\n",
" <td>52</td>\n",
" <td>23</td>\n",
" <td>52</td>\n",
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" <td>problem</td>\n",
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" <td>29</td>\n",
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" <td>headache</td>\n",
" <td>43</td>\n",
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" <th>457</th>\n",
" <td>176318078_a</td>\n",
" <td>marked hyperkalemia</td>\n",
" <td>88</td>\n",
" <td>10</td>\n",
" <td>88</td>\n",
" <td>11</td>\n",
" <td>problem</td>\n",
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" <td>176318078_a</td>\n",
" <td>epistaxis</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>problem</td>\n",
" <td>conditional</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6756</th>\n",
" <td>record-80</td>\n",
" <td>dusky</td>\n",
" <td>25</td>\n",
" <td>14</td>\n",
" <td>25</td>\n",
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" <td>problem</td>\n",
" <td>conditional</td>\n",
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" <tr>\n",
" <th>6953</th>\n",
" <td>record-83</td>\n",
" <td>only mild nausea</td>\n",
" <td>42</td>\n",
" <td>7</td>\n",
" <td>42</td>\n",
" <td>9</td>\n",
" <td>problem</td>\n",
" <td>conditional</td>\n",
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" <tr>\n",
" <th>6983</th>\n",
" <td>record-83</td>\n",
" <td>occ nausea</td>\n",
" <td>37</td>\n",
" <td>10</td>\n",
" <td>37</td>\n",
" <td>11</td>\n",
" <td>problem</td>\n",
" <td>conditional</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6995</th>\n",
" <td>record-84</td>\n",
" <td>dyspnea</td>\n",
" <td>25</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12</td>\n",
" <td>problem</td>\n",
" <td>conditional</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7065</th>\n",
" <td>record-84</td>\n",
" <td>dyspnea</td>\n",
" <td>86</td>\n",
" <td>15</td>\n",
" <td>86</td>\n",
" <td>15</td>\n",
" <td>problem</td>\n",
" <td>conditional</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>73 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line \\\n",
"345 143748600_SC episodes of atypical cp x 1 week 52 \n",
"442 176318078_a bleeding from the mouth and nose 29 \n",
"454 176318078_a headache 43 \n",
"457 176318078_a marked hyperkalemia 88 \n",
"461 176318078_a epistaxis 21 \n",
"... ... ... ... \n",
"6756 record-80 dusky 25 \n",
"6953 record-83 only mild nausea 42 \n",
"6983 record-83 occ nausea 37 \n",
"6995 record-84 dyspnea 25 \n",
"7065 record-84 dyspnea 86 \n",
"\n",
" start_word_number end_line end_word_number concept_type assertion_type \n",
"345 23 52 29 problem conditional \n",
"442 3 29 8 problem conditional \n",
"454 3 43 3 problem conditional \n",
"457 10 88 11 problem conditional \n",
"461 0 21 0 problem conditional \n",
"... ... ... ... ... ... \n",
"6756 14 25 14 problem conditional \n",
"6953 7 42 9 problem conditional \n",
"6983 10 37 11 problem conditional \n",
"6995 12 25 12 problem conditional \n",
"7065 15 86 15 problem conditional \n",
"\n",
"[73 rows x 8 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertion_df[assertion_df[\"assertion_type\"] == \"conditional\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Hypothetical:\n",
"medical problems that the note asserts the patient may\n",
"develop."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
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" <td>018636330_DH</td>\n",
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" <td>50</td>\n",
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" <th>20</th>\n",
" <td>018636330_DH</td>\n",
" <td>bowel and bladder disruption</td>\n",
" <td>50</td>\n",
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" <td>50</td>\n",
" <td>20</td>\n",
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" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7028</th>\n",
" <td>record-84</td>\n",
" <td>shortness of breath</td>\n",
" <td>99</td>\n",
" <td>25</td>\n",
" <td>99</td>\n",
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" <tr>\n",
" <th>7029</th>\n",
" <td>record-84</td>\n",
" <td>any chest pain</td>\n",
" <td>87</td>\n",
" <td>12</td>\n",
" <td>87</td>\n",
" <td>14</td>\n",
" <td>problem</td>\n",
" <td>hypothetical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7040</th>\n",
" <td>record-84</td>\n",
" <td>difficulty breathing</td>\n",
" <td>87</td>\n",
" <td>18</td>\n",
" <td>87</td>\n",
" <td>19</td>\n",
" <td>problem</td>\n",
" <td>hypothetical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7041</th>\n",
" <td>record-84</td>\n",
" <td>pressure</td>\n",
" <td>87</td>\n",
" <td>16</td>\n",
" <td>87</td>\n",
" <td>16</td>\n",
" <td>problem</td>\n",
" <td>hypothetical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7045</th>\n",
" <td>record-84</td>\n",
" <td>light-headiness</td>\n",
" <td>87</td>\n",
" <td>23</td>\n",
" <td>87</td>\n",
" <td>23</td>\n",
" <td>problem</td>\n",
" <td>hypothetical</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>382 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line start_word_number \\\n",
"0 018636330_DH pain 55 10 \n",
"4 018636330_DH chills 50 9 \n",
"18 018636330_DH fevers 50 7 \n",
"19 018636330_DH numbness 50 14 \n",
"20 018636330_DH bowel and bladder disruption 50 17 \n",
"... ... ... ... ... \n",
"7028 record-84 shortness of breath 99 25 \n",
"7029 record-84 any chest pain 87 12 \n",
"7040 record-84 difficulty breathing 87 18 \n",
"7041 record-84 pressure 87 16 \n",
"7045 record-84 light-headiness 87 23 \n",
"\n",
" end_line end_word_number concept_type assertion_type \n",
"0 55 10 problem hypothetical \n",
"4 50 9 problem hypothetical \n",
"18 50 7 problem hypothetical \n",
"19 50 14 problem hypothetical \n",
"20 50 20 problem hypothetical \n",
"... ... ... ... ... \n",
"7028 99 27 problem hypothetical \n",
"7029 87 14 problem hypothetical \n",
"7040 87 19 problem hypothetical \n",
"7041 87 16 problem hypothetical \n",
"7045 87 23 problem hypothetical \n",
"\n",
"[382 rows x 8 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertion_df[assertion_df[\"assertion_type\"] == \"hypothetical\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Not associated with Patient:\n",
"the mention of the medical problem is associated\n",
"with someone who is not the patient."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>concept_text</th>\n",
" <th>start_line</th>\n",
" <th>start_word_number</th>\n",
" <th>end_line</th>\n",
" <th>end_word_number</th>\n",
" <th>concept_type</th>\n",
" <th>assertion_type</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>101407944_PUMC</td>\n",
" <td>cva</td>\n",
" <td>49</td>\n",
" <td>8</td>\n",
" <td>49</td>\n",
" <td>8</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>101407944_PUMC</td>\n",
" <td>diabetes</td>\n",
" <td>49</td>\n",
" <td>6</td>\n",
" <td>49</td>\n",
" <td>6</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
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" <td>hypercholesterolemia</td>\n",
" <td>49</td>\n",
" <td>10</td>\n",
" <td>49</td>\n",
" <td>10</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>101407944_PUMC</td>\n",
" <td>cad</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" <tr>\n",
" <th>245</th>\n",
" <td>130959255</td>\n",
" <td>heart disease</td>\n",
" <td>38</td>\n",
" <td>8</td>\n",
" <td>38</td>\n",
" <td>9</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6809</th>\n",
" <td>record-81</td>\n",
" <td>mentally handicapped</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>4</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6827</th>\n",
" <td>record-81</td>\n",
" <td>mentally handicapped</td>\n",
" <td>49</td>\n",
" <td>11</td>\n",
" <td>49</td>\n",
" <td>12</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6831</th>\n",
" <td>record-81</td>\n",
" <td>human immunodeficiency virus</td>\n",
" <td>47</td>\n",
" <td>20</td>\n",
" <td>47</td>\n",
" <td>22</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7031</th>\n",
" <td>record-84</td>\n",
" <td>an mi</td>\n",
" <td>33</td>\n",
" <td>6</td>\n",
" <td>33</td>\n",
" <td>7</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7060</th>\n",
" <td>record-84</td>\n",
" <td>coronary artery disease</td>\n",
" <td>12</td>\n",
" <td>19</td>\n",
" <td>12</td>\n",
" <td>21</td>\n",
" <td>problem</td>\n",
" <td>associated_with_someone_else</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>89 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" filename concept_text start_line \\\n",
"133 101407944_PUMC cva 49 \n",
"136 101407944_PUMC diabetes 49 \n",
"146 101407944_PUMC hypercholesterolemia 49 \n",
"199 101407944_PUMC cad 49 \n",
"245 130959255 heart disease 38 \n",
"... ... ... ... \n",
"6809 record-81 mentally handicapped 30 \n",
"6827 record-81 mentally handicapped 49 \n",
"6831 record-81 human immunodeficiency virus 47 \n",
"7031 record-84 an mi 33 \n",
"7060 record-84 coronary artery disease 12 \n",
"\n",
" start_word_number end_line end_word_number concept_type \\\n",
"133 8 49 8 problem \n",
"136 6 49 6 problem \n",
"146 10 49 10 problem \n",
"199 2 49 2 problem \n",
"245 8 38 9 problem \n",
"... ... ... ... ... \n",
"6809 3 30 4 problem \n",
"6827 11 49 12 problem \n",
"6831 20 47 22 problem \n",
"7031 6 33 7 problem \n",
"7060 19 12 21 problem \n",
"\n",
" assertion_type \n",
"133 associated_with_someone_else \n",
"136 associated_with_someone_else \n",
"146 associated_with_someone_else \n",
"199 associated_with_someone_else \n",
"245 associated_with_someone_else \n",
"... ... \n",
"6809 associated_with_someone_else \n",
"6827 associated_with_someone_else \n",
"6831 associated_with_someone_else \n",
"7031 associated_with_someone_else \n",
"7060 associated_with_someone_else \n",
"\n",
"[89 rows x 8 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assertion_df[assertion_df[\"assertion_type\"] == \"associated_with_someone_else\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Relations Analysis"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'concept_text_1': ['po pain medications', 'a postoperative ct scan', 'percocet', 'c5-6 disc herniation', 'c5-6 disc herniation', 'a c5-6 disc herniation', 'an mri', 'an mri', 'an mri', 'her exam'], 'start_line_1': [47, 43, 55, 21, 21, 27, 27, 27, 27, 44], 'start_word_number_1': [7, 2, 1, 0, 0, 11, 8, 8, 8, 3], 'end_line_1': [47, 43, 55, 21, 21, 27, 27, 27, 27, 44], 'end_word_number_1': [9, 5, 1, 2, 2, 14, 9, 9, 9, 4], 'concept_text_2': ['her pain', 'partial decompression of the spinal canal', 'pain', 'cord compression', 'myelopathy', 'cord compression', 'a c5-6 disc herniation', 'cord compression', 'a t2 signal change', 'her hyperreflexia'], 'start_line_2': [47, 43, 55, 21, 21, 27, 27, 27, 27, 44], 'start_word_number_2': [0, 8, 10, 4, 7, 16, 11, 16, 19, 9], 'end_line_2': [47, 43, 55, 21, 21, 27, 27, 27, 27, 44], 'end_word_number_2': [1, 13, 10, 5, 7, 17, 14, 17, 22, 10], 'relation_type': ['TrIP', 'TeRP', 'TrAP', 'PIP', 'PIP', 'PIP', 'TeRP', 'TeRP', 'TeRP', 'TeCP']}\n"
]
}
],
"source": [
"print(df.rel[0])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"language": "python",
"name": "python3"
},
"language_info": {
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"file_extension": ".py",
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