{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Studying pulmonary tuberculosis using the MIMIC-III database" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# Init\n", "\n", "import pandas as pd\n", "\n", "pd.set_option('display.max_columns', None) \n", "pd.set_option('display.max_rows', None) \n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(651047, 5)\n" ] }, { "data": { "text/html": [ "
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ROW_IDSUBJECT_IDHADM_IDSEQ_NUMICD9_CODE
012971091723351.040301
112981091723352.0486
212991091723353.058281
313001091723354.05855
413011091723355.04254
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" ], "text/plain": [ " ROW_ID SUBJECT_ID HADM_ID SEQ_NUM ICD9_CODE\n", "0 1297 109 172335 1.0 40301\n", "1 1298 109 172335 2.0 486\n", "2 1299 109 172335 3.0 58281\n", "3 1300 109 172335 4.0 5855\n", "4 1301 109 172335 5.0 4254" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Table ICD-9 Diagnoses\n", "\n", "icd = pd.read_csv(\"DIAGNOSES_ICD.csv\")\n", "print(icd.shape)\n", "icd.head()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(14567, 4)\n" ] }, { "data": { "text/html": [ "
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ROW_IDICD9_CODESHORT_TITLELONG_TITLE
017401166TB pneumonia-oth testTuberculous pneumonia [any form], tubercle bac...
117501170TB pneumothorax-unspecTuberculous pneumothorax, unspecified
217601171TB pneumothorax-no examTuberculous pneumothorax, bacteriological or h...
317701172TB pneumothorx-exam unknTuberculous pneumothorax, bacteriological or h...
417801173TB pneumothorax-micro dxTuberculous pneumothorax, tubercle bacilli fou...
\n", "
" ], "text/plain": [ " ROW_ID ICD9_CODE SHORT_TITLE \\\n", "0 174 01166 TB pneumonia-oth test \n", "1 175 01170 TB pneumothorax-unspec \n", "2 176 01171 TB pneumothorax-no exam \n", "3 177 01172 TB pneumothorx-exam unkn \n", "4 178 01173 TB pneumothorax-micro dx \n", "\n", " LONG_TITLE \n", "0 Tuberculous pneumonia [any form], tubercle bac... \n", "1 Tuberculous pneumothorax, unspecified \n", "2 Tuberculous pneumothorax, bacteriological or h... \n", "3 Tuberculous pneumothorax, bacteriological or h... \n", "4 Tuberculous pneumothorax, tubercle bacilli fou... " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Table Diagnoses Descriptions\n", "\n", "dicd = pd.read_csv(\"D_ICD_DIAGNOSES.csv\")\n", "print(dicd.shape)\n", "dicd.head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15, 4)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":5: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " tuberc.drop([\"ROW_ID\"],axis=1, inplace=True)\n" ] }, { "data": { "text/html": [ "
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SUBJECT_IDHADM_IDSEQ_NUMICD9_CODE
130137121391488093.001190
130138121391488094.001136
176061150461047525.001194
226729211921402608.001190
229369225321678531.001193
325087305441190173.001194
336906281201204092.001164
443439528311069393.001190
454775575991801504.001190
4602705870211424618.001190
4806626394418791315.001123
498832689461829549.001190
571194862261069152.001190
582658843181886045.001186
599773898401862119.001190
\n", "
" ], "text/plain": [ " SUBJECT_ID HADM_ID SEQ_NUM ICD9_CODE\n", "130137 12139 148809 3.0 01190\n", "130138 12139 148809 4.0 01136\n", "176061 15046 104752 5.0 01194\n", "226729 21192 140260 8.0 01190\n", "229369 22532 167853 1.0 01193\n", "325087 30544 119017 3.0 01194\n", "336906 28120 120409 2.0 01164\n", "443439 52831 106939 3.0 01190\n", "454775 57599 180150 4.0 01190\n", "460270 58702 114246 18.0 01190\n", "480662 63944 187913 15.0 01123\n", "498832 68946 182954 9.0 01190\n", "571194 86226 106915 2.0 01190\n", "582658 84318 188604 5.0 01186\n", "599773 89840 186211 9.0 01190" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pulmonary tuberculosis: ICD9 011\n", "\n", "tuberc = icd[~icd.ICD9_CODE.isna()]\n", "tuberc = icd[icd.ICD9_CODE.str.startswith(\"011\")==True]\n", "tuberc.drop([\"ROW_ID\"],axis=1, inplace=True)\n", "print(tuberc.shape)\n", "tuberc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# DO NOT RUN THIS CELL! (Table too big)\n", "\n", "# Table ChartEvents\n", "\n", "cev = pd.read_csv(\"CHARTEVENTS.csv.gz\")\n", "cev.drop([\"ROW_ID\"], axis=1, inplace=True)\n", "tcev = pd.merge(tuberc, cev, how='inner', on=['SUBJECT_ID'])\n", "tcev.head()\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":3: DtypeWarning: Columns (7,9,17,20,21) have mixed types. Specify dtype option on import or set low_memory=False.\n", " iev = pd.read_csv(\"INPUTEVENTS_CV.csv.gz\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(7623, 24)\n" ] }, { "data": { "text/html": [ "
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SUBJECT_IDHADM_ID_xSEQ_NUMICD9_CODEHADM_ID_yICUSTAY_IDCHARTTIMEITEMIDAMOUNTAMOUNTUOMRATERATEUOMSTORETIMECGIDORDERIDLINKORDERIDSTOPPEDNEWBOTTLEORIGINALAMOUNTORIGINALAMOUNTUOMORIGINALROUTEORIGINALRATEORIGINALRATEUOMORIGINALSITE
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2121391488093.001190148809.0240663.02152-05-30 06:00:003001350.0mlNaNNaN2152-05-30 05:44:0020706.015553794704410NaNNaN100.0mlIntravenous PushNaNNaNNaN
3121391488093.001190148809.0240663.02152-05-30 08:00:0030013300.0mlNaNNaN2152-05-30 08:01:0016890.089429384704410NaNNaN100.0mlIntravenous PushNaNNaNNaN
4121391488093.001190148809.0240663.02152-05-30 18:00:0030013100.0mlNaNNaN2152-05-30 18:40:0017480.081818764704410NaNNaN100.0mlIntravenous PushNaNNaNNaN
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" ], "text/plain": [ " SUBJECT_ID HADM_ID_x SEQ_NUM ICD9_CODE HADM_ID_y ICUSTAY_ID \\\n", "0 12139 148809 3.0 01190 148809.0 240663.0 \n", "1 12139 148809 3.0 01190 148809.0 240663.0 \n", "2 12139 148809 3.0 01190 148809.0 240663.0 \n", "3 12139 148809 3.0 01190 148809.0 240663.0 \n", "4 12139 148809 3.0 01190 148809.0 240663.0 \n", "\n", " CHARTTIME ITEMID AMOUNT AMOUNTUOM RATE RATEUOM \\\n", "0 2152-05-26 04:00:00 30056 100.0 ml NaN NaN \n", "1 2152-05-26 08:00:00 30056 100.0 ml NaN NaN \n", "2 2152-05-30 06:00:00 30013 50.0 ml NaN NaN \n", "3 2152-05-30 08:00:00 30013 300.0 ml NaN NaN \n", "4 2152-05-30 18:00:00 30013 100.0 ml NaN NaN \n", "\n", " STORETIME CGID ORDERID LINKORDERID STOPPED NEWBOTTLE \\\n", "0 2152-05-26 03:53:00 15477.0 11668241 11668241 NaN NaN \n", "1 2152-05-26 08:10:00 19150.0 7762335 11668241 NaN NaN \n", "2 2152-05-30 05:44:00 20706.0 1555379 4704410 NaN NaN \n", "3 2152-05-30 08:01:00 16890.0 8942938 4704410 NaN NaN \n", "4 2152-05-30 18:40:00 17480.0 8181876 4704410 NaN NaN \n", "\n", " ORIGINALAMOUNT ORIGINALAMOUNTUOM ORIGINALROUTE ORIGINALRATE \\\n", "0 NaN ml Oral NaN \n", "1 NaN ml Oral NaN \n", "2 100.0 ml Intravenous Push NaN \n", "3 100.0 ml Intravenous Push NaN \n", "4 100.0 ml Intravenous Push NaN \n", "\n", " ORIGINALRATEUOM ORIGINALSITE \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Table InputEvents\n", "\n", "iev = pd.read_csv(\"INPUTEVENTS_CV.csv.gz\")\n", "iev.drop([\"ROW_ID\"], axis=1, inplace=True)\n", "tiev = pd.merge(tuberc, iev, how=\"inner\", on=[\"SUBJECT_ID\"])\n", "tiev.drop_duplicates()\n", "print(tiev.shape)\n", "tiev.head()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SUBJECT_IDHADM_ID_xSEQ_NUMICD9_CODEHADM_ID_yICUSTAY_IDCHARTTIMEITEMIDAMOUNTAMOUNTUOMRATERATEUOMSTORETIMECGIDORDERIDLINKORDERIDSTOPPEDNEWBOTTLEORIGINALAMOUNTORIGINALAMOUNTUOMORIGINALROUTEORIGINALRATEORIGINALRATEUOMORIGINALSITE
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uniqueNaNNaNNaN5NaNNaN1138NaNNaN5NaN41047NaNNaNNaN3NaNNaN86NaN10
topNaNNaNNaN01194NaNNaN2152-05-26 20:00:00NaNNaNmlNaNmcghr2152-05-26 19:18:00NaNNaNNaND/C'dNaNNaNmlIntravenous PushNaNml/hrNaN
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" ], "text/plain": [ " SUBJECT_ID HADM_ID_x SEQ_NUM ICD9_CODE HADM_ID_y \\\n", "count 7623.000000 7623.000000 7623.000000 7623 7623.000000 \n", "unique NaN NaN NaN 5 NaN \n", "top NaN NaN NaN 01194 NaN \n", "freq NaN NaN NaN 5247 NaN \n", "mean 14954.734881 118716.719139 4.446150 NaN 123578.953168 \n", "std 3452.643050 20475.074860 0.944792 NaN 19062.147291 \n", "min 12139.000000 104752.000000 1.000000 NaN 103875.000000 \n", "25% 12139.000000 104752.000000 4.000000 NaN 104752.000000 \n", "50% 15046.000000 104752.000000 5.000000 NaN 116990.000000 \n", "75% 15046.000000 148809.000000 5.000000 NaN 148809.000000 \n", "max 30544.000000 167853.000000 8.000000 NaN 190121.000000 \n", "\n", " ICUSTAY_ID CHARTTIME ITEMID AMOUNT \\\n", "count 7623.000000 7623 7623.000000 5668.000000 \n", "unique NaN 1138 NaN NaN \n", "top NaN 2152-05-26 20:00:00 NaN NaN \n", "freq NaN 34 NaN NaN \n", "mean 241767.115309 NaN 30086.923914 47.035957 \n", "std 16577.010373 NaN 73.960937 131.178115 \n", "min 212957.000000 NaN 30001.000000 0.000000 \n", "25% 223314.000000 NaN 30018.000000 4.000000 \n", "50% 240663.000000 NaN 30118.000000 10.000000 \n", "75% 247246.000000 NaN 30124.000000 55.000000 \n", "max 299299.000000 NaN 30354.000000 5000.000000 \n", "\n", " AMOUNTUOM RATE RATEUOM STORETIME CGID \\\n", "count 5679 1897.000000 1900 7623 7613.000000 \n", "unique 5 NaN 4 1047 NaN \n", "top ml NaN mcghr 2152-05-26 19:18:00 NaN \n", "freq 3975 NaN 816 94 NaN \n", "mean NaN 49.194149 NaN NaN 18279.038881 \n", "std NaN 92.136112 NaN NaN 2409.378502 \n", "min NaN 0.000000 NaN NaN 14411.000000 \n", "25% NaN 2.500000 NaN NaN 15225.000000 \n", "50% NaN 5.000000 NaN NaN 19150.000000 \n", "75% NaN 75.000000 NaN NaN 20115.000000 \n", "max NaN 1000.000000 NaN NaN 21570.000000 \n", "\n", " ORDERID LINKORDERID STOPPED NEWBOTTLE ORIGINALAMOUNT \\\n", "count 7.623000e+03 7.623000e+03 126 83.0 4786.000000 \n", "unique NaN NaN 3 NaN NaN \n", "top NaN NaN D/C'd NaN NaN \n", "freq NaN NaN 66 NaN NaN \n", "mean 5.958692e+06 6.251904e+06 NaN 1.0 690.673214 \n", "std 3.389261e+06 3.332983e+06 NaN 0.0 1397.939263 \n", "min 4.890000e+02 1.000940e+05 NaN 1.0 4.000000 \n", "25% 3.079272e+06 3.198685e+06 NaN 1.0 50.000000 \n", "50% 5.999397e+06 6.167807e+06 NaN 1.0 250.000000 \n", "75% 8.865809e+06 1.021762e+07 NaN 1.0 1000.000000 \n", "max 1.195434e+07 1.189472e+07 NaN 1.0 25000.000000 \n", "\n", " ORIGINALAMOUNTUOM ORIGINALROUTE ORIGINALRATE ORIGINALRATEUOM \\\n", "count 5723 7623 2486.000000 2486 \n", "unique 8 6 NaN 1 \n", "top ml Intravenous Push NaN ml/hr \n", "freq 3316 4265 NaN 2486 \n", "mean NaN NaN 35.926066 NaN \n", "std NaN NaN 94.819152 NaN \n", "min NaN NaN 1.000000 NaN \n", "25% NaN NaN 2.000000 NaN \n", "50% NaN NaN 10.000000 NaN \n", "75% NaN NaN 45.000000 NaN \n", "max NaN NaN 1000.000000 NaN \n", "\n", " ORIGINALSITE \n", "count 0 \n", "unique 0 \n", "top NaN \n", "freq NaN \n", "mean NaN \n", "std NaN \n", "min NaN \n", "25% NaN \n", "50% NaN \n", "75% NaN \n", "max NaN " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiev.describe(include=\"all\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":6: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " p.set_xticklabels(p.get_xticklabels(), rotation=90)\n" ] }, { "data": { "text/plain": [ "[Text(0, 0, 'Oral'),\n", " Text(1, 0, 'Intravenous Push'),\n", " Text(2, 0, 'Gastric/Feeding Tube'),\n", " Text(3, 0, 'Nasogastric'),\n", " Text(4, 0, 'IV Drip'),\n", " Text(5, 0, 'Intravenous Infusion')]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# input intervention\n", "\n", "import seaborn as sns\n", "\n", "p = sns.histplot(data=tiev,x=\"ORIGINALROUTE\")\n", "p.set_xticklabels(p.get_xticklabels(), rotation=90)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " p.set_xticklabels(p.get_xticklabels(), rotation=90)\n" ] }, { "data": { "text/plain": [ "[Text(0, 0, 'Po Intake'),\n", " Text(1, 0, 'D5W'),\n", " Text(2, 0, 'Lactated Ringers'),\n", " Text(3, 0, 'TF Residual'),\n", " Text(4, 0, '.9% Normal Saline'),\n", " Text(5, 0, 'Gastric Meds'),\n", " Text(6, 0, 'Free Water Bolus'),\n", " Text(7, 0, 'Promote w/fiber'),\n", " Text(8, 0, 'Hespan'),\n", " Text(9, 0, 'Dopamine'),\n", " Text(10, 0, 'Fentanyl'),\n", " Text(11, 0, 'Levophed-k'),\n", " Text(12, 0, 'Neosynephrine-k'),\n", " Text(13, 0, 'Midazolam'),\n", " Text(14, 0, \"Packed RBC's\"),\n", " Text(15, 0, 'D5/.45NS'),\n", " Text(16, 0, 'Pre-Admission Intake'),\n", " Text(17, 0, 'Nepro'),\n", " Text(18, 0, 'GT Flush'),\n", " Text(19, 0, 'Boost Plus'),\n", " Text(20, 0, 'ProBalance'),\n", " Text(21, 0, 'Sodium Bicarbonate'),\n", " Text(22, 0, 'Propofol'),\n", " Text(23, 0, 'Potassium Phosphate'),\n", " Text(24, 0, 'D5NS'),\n", " Text(25, 0, 'PACU Crystalloids'),\n", " Text(26, 0, 'Replete w/fiber'),\n", " Text(27, 0, 'Heparin'),\n", " Text(28, 0, 'KCL')]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Table items descriptions\n", "\n", "ditem = pd.read_csv(\"D_ITEMS.csv\")\n", "dict_df = ditem.set_index('ITEMID')['LABEL'].to_dict()\n", "tiev[\"ditem\"] = tiev[\"ITEMID\"].map(dict_df)\n", "\n", "p = sns.histplot(data=tiev,x=\"ditem\")\n", "p.set_xticklabels(p.get_xticklabels(), rotation=90)\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ROW_IDITEMIDLABELABBREVIATIONDBSOURCELINKSTOCATEGORYUNITNAMEPARAM_TYPECONCEPTID
0457497Patient controlled analgesia (PCA) [Inject]NaNcarevuecharteventsNaNNaNNaNNaN
1458498PCA Lockout (Min)NaNcarevuecharteventsNaNNaNNaNNaN
2459499PCA MedicationNaNcarevuecharteventsNaNNaNNaNNaN
3460500PCA Total DoseNaNcarevuecharteventsNaNNaNNaNNaN
4461501PCV Exh Vt (Obser)NaNcarevuecharteventsNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " ROW_ID ITEMID LABEL ABBREVIATION \\\n", "0 457 497 Patient controlled analgesia (PCA) [Inject] NaN \n", "1 458 498 PCA Lockout (Min) NaN \n", "2 459 499 PCA Medication NaN \n", "3 460 500 PCA Total Dose NaN \n", "4 461 501 PCV Exh Vt (Obser) NaN \n", "\n", " DBSOURCE LINKSTO CATEGORY UNITNAME PARAM_TYPE CONCEPTID \n", "0 carevue chartevents NaN NaN NaN NaN \n", "1 carevue chartevents NaN NaN NaN NaN \n", "2 carevue chartevents NaN NaN NaN NaN \n", "3 carevue chartevents NaN NaN NaN NaN \n", "4 carevue chartevents NaN NaN NaN NaN " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Table D_ITEMS\n", "\n", "ditem.head()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(61532, 12)\n" ] }, { "data": { "text/html": [ "
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ROW_IDSUBJECT_IDHADM_IDICUSTAY_IDDBSOURCEFIRST_CAREUNITLAST_CAREUNITFIRST_WARDIDLAST_WARDIDINTIMEOUTTIMELOS
0365268110404280836carevueMICUMICU52522198-02-14 23:27:382198-02-18 05:26:113.2490
1366269106296206613carevueMICUMICU52522170-11-05 11:05:292170-11-08 17:46:573.2788
2367270188028220345carevueCCUCCU57572128-06-24 15:05:202128-06-27 12:32:292.8939
3368271173727249196carevueMICUSICU52232120-08-07 23:12:422120-08-10 00:39:042.0600
4369272164716210407carevueCCUCCU57572186-12-25 21:08:042186-12-27 12:01:131.6202
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" ], "text/plain": [ " ROW_ID SUBJECT_ID HADM_ID ICUSTAY_ID DBSOURCE FIRST_CAREUNIT \\\n", "0 365 268 110404 280836 carevue MICU \n", "1 366 269 106296 206613 carevue MICU \n", "2 367 270 188028 220345 carevue CCU \n", "3 368 271 173727 249196 carevue MICU \n", "4 369 272 164716 210407 carevue CCU \n", "\n", " LAST_CAREUNIT FIRST_WARDID LAST_WARDID INTIME \\\n", "0 MICU 52 52 2198-02-14 23:27:38 \n", "1 MICU 52 52 2170-11-05 11:05:29 \n", "2 CCU 57 57 2128-06-24 15:05:20 \n", "3 SICU 52 23 2120-08-07 23:12:42 \n", "4 CCU 57 57 2186-12-25 21:08:04 \n", "\n", " OUTTIME LOS \n", "0 2198-02-18 05:26:11 3.2490 \n", "1 2170-11-08 17:46:57 3.2788 \n", "2 2128-06-27 12:32:29 2.8939 \n", "3 2120-08-10 00:39:04 2.0600 \n", "4 2186-12-27 12:01:13 1.6202 " ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Table ICUStays\n", "\n", "icu = pd.read_csv(\"ICUSTAYS.csv\")\n", "print(icu.shape)\n", "icu.head()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(48300, 34)\n" ] }, { "data": { "text/html": [ "
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SUBJECT_IDHADM_ID_xSEQ_NUMICD9_CODEHADM_ID_yICUSTAY_ID_xCHARTTIMEITEMIDAMOUNTAMOUNTUOMRATERATEUOMSTORETIMECGIDORDERIDLINKORDERIDSTOPPEDNEWBOTTLEORIGINALAMOUNTORIGINALAMOUNTUOMORIGINALROUTEORIGINALRATEORIGINALRATEUOMORIGINALSITEHADM_IDICUSTAY_ID_yDBSOURCEFIRST_CAREUNITLAST_CAREUNITFIRST_WARDIDLAST_WARDIDINTIMEOUTTIMELOS
0121391488093.001190148809.0240663.02152-05-26 04:00:0030056100.0mlNaNNaN2152-05-26 03:53:0015477.01166824111668241NaNNaNNaNmlOralNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.798
1121391488093.001190148809.0240663.02152-05-26 08:00:0030056100.0mlNaNNaN2152-05-26 08:10:0019150.0776233511668241NaNNaNNaNmlOralNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.798
2121391488093.001190148809.0240663.02152-05-30 06:00:003001350.0mlNaNNaN2152-05-30 05:44:0020706.015553794704410NaNNaN100.0mlIntravenous PushNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.798
3121391488093.001190148809.0240663.02152-05-30 08:00:0030013300.0mlNaNNaN2152-05-30 08:01:0016890.089429384704410NaNNaN100.0mlIntravenous PushNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.798
4121391488093.001190148809.0240663.02152-05-30 18:00:0030013100.0mlNaNNaN2152-05-30 18:40:0017480.081818764704410NaNNaN100.0mlIntravenous PushNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.798
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" ], "text/plain": [ " SUBJECT_ID HADM_ID_x SEQ_NUM ICD9_CODE HADM_ID_y ICUSTAY_ID_x \\\n", "0 12139 148809 3.0 01190 148809.0 240663.0 \n", "1 12139 148809 3.0 01190 148809.0 240663.0 \n", "2 12139 148809 3.0 01190 148809.0 240663.0 \n", "3 12139 148809 3.0 01190 148809.0 240663.0 \n", "4 12139 148809 3.0 01190 148809.0 240663.0 \n", "\n", " CHARTTIME ITEMID AMOUNT AMOUNTUOM RATE RATEUOM \\\n", "0 2152-05-26 04:00:00 30056 100.0 ml NaN NaN \n", "1 2152-05-26 08:00:00 30056 100.0 ml NaN NaN \n", "2 2152-05-30 06:00:00 30013 50.0 ml NaN NaN \n", "3 2152-05-30 08:00:00 30013 300.0 ml NaN NaN \n", "4 2152-05-30 18:00:00 30013 100.0 ml NaN NaN \n", "\n", " STORETIME CGID ORDERID LINKORDERID STOPPED NEWBOTTLE \\\n", "0 2152-05-26 03:53:00 15477.0 11668241 11668241 NaN NaN \n", "1 2152-05-26 08:10:00 19150.0 7762335 11668241 NaN NaN \n", "2 2152-05-30 05:44:00 20706.0 1555379 4704410 NaN NaN \n", "3 2152-05-30 08:01:00 16890.0 8942938 4704410 NaN NaN \n", "4 2152-05-30 18:40:00 17480.0 8181876 4704410 NaN NaN \n", "\n", " ORIGINALAMOUNT ORIGINALAMOUNTUOM ORIGINALROUTE ORIGINALRATE \\\n", "0 NaN ml Oral NaN \n", "1 NaN ml Oral NaN \n", "2 100.0 ml Intravenous Push NaN \n", "3 100.0 ml Intravenous Push NaN \n", "4 100.0 ml Intravenous Push NaN \n", "\n", " ORIGINALRATEUOM ORIGINALSITE HADM_ID ICUSTAY_ID_y DBSOURCE FIRST_CAREUNIT \\\n", "0 NaN NaN 148809 240663 carevue MICU \n", "1 NaN NaN 148809 240663 carevue MICU \n", "2 NaN NaN 148809 240663 carevue MICU \n", "3 NaN NaN 148809 240663 carevue MICU \n", "4 NaN NaN 148809 240663 carevue MICU \n", "\n", " LAST_CAREUNIT FIRST_WARDID LAST_WARDID INTIME \\\n", "0 MICU 52 52 2152-05-25 23:06:09 \n", "1 MICU 52 52 2152-05-25 23:06:09 \n", "2 MICU 52 52 2152-05-25 23:06:09 \n", "3 MICU 52 52 2152-05-25 23:06:09 \n", "4 MICU 52 52 2152-05-25 23:06:09 \n", "\n", " OUTTIME LOS \n", "0 2152-05-31 18:15:14 5.798 \n", "1 2152-05-31 18:15:14 5.798 \n", "2 2152-05-31 18:15:14 5.798 \n", "3 2152-05-31 18:15:14 5.798 \n", "4 2152-05-31 18:15:14 5.798 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Take only patients that are in tuberc table\n", "\n", "icu.drop([\"ROW_ID\"], axis=1, inplace=True)\n", "ticu = pd.merge(tiev, icu, how=\"inner\", on=[\"SUBJECT_ID\"])\n", "ticu.drop_duplicates()\n", "print(ticu.shape)\n", "ticu.head()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(46520, 8)\n" ] }, { "data": { "text/html": [ "
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ROW_IDSUBJECT_IDGENDERDOBDODDOD_HOSPDOD_SSNEXPIRE_FLAG
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2236251M2090-03-15 00:00:00NaNNaNNaN0
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" ], "text/plain": [ " ROW_ID SUBJECT_ID GENDER DOB DOD \\\n", "0 234 249 F 2075-03-13 00:00:00 NaN \n", "1 235 250 F 2164-12-27 00:00:00 2188-11-22 00:00:00 \n", "2 236 251 M 2090-03-15 00:00:00 NaN \n", "3 237 252 M 2078-03-06 00:00:00 NaN \n", "4 238 253 F 2089-11-26 00:00:00 NaN \n", "\n", " DOD_HOSP DOD_SSN EXPIRE_FLAG \n", "0 NaN NaN 0 \n", "1 2188-11-22 00:00:00 NaN 1 \n", "2 NaN NaN 0 \n", "3 NaN NaN 0 \n", "4 NaN NaN 0 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pat = pd.read_csv(\"PATIENTS.csv\")\n", "print(pat.shape)\n", "pat.head()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(48300, 41)\n" ] }, { "data": { "text/html": [ "
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SUBJECT_IDHADM_ID_xSEQ_NUMICD9_CODEHADM_ID_yICUSTAY_ID_xCHARTTIMEITEMIDAMOUNTAMOUNTUOMRATERATEUOMSTORETIMECGIDORDERIDLINKORDERIDSTOPPEDNEWBOTTLEORIGINALAMOUNTORIGINALAMOUNTUOMORIGINALROUTEORIGINALRATEORIGINALRATEUOMORIGINALSITEHADM_IDICUSTAY_ID_yDBSOURCEFIRST_CAREUNITLAST_CAREUNITFIRST_WARDIDLAST_WARDIDINTIMEOUTTIMELOSROW_IDGENDERDOBDODDOD_HOSPDOD_SSNEXPIRE_FLAG
0121391488093.001190148809.0240663.02152-05-26 04:00:0030056100.0mlNaNNaN2152-05-26 03:53:0015477.01166824111668241NaNNaNNaNmlOralNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.79811489M2092-01-24 00:00:00NaNNaNNaN0
1121391488093.001190148809.0240663.02152-05-26 08:00:0030056100.0mlNaNNaN2152-05-26 08:10:0019150.0776233511668241NaNNaNNaNmlOralNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.79811489M2092-01-24 00:00:00NaNNaNNaN0
2121391488093.001190148809.0240663.02152-05-30 06:00:003001350.0mlNaNNaN2152-05-30 05:44:0020706.015553794704410NaNNaN100.0mlIntravenous PushNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.79811489M2092-01-24 00:00:00NaNNaNNaN0
3121391488093.001190148809.0240663.02152-05-30 08:00:0030013300.0mlNaNNaN2152-05-30 08:01:0016890.089429384704410NaNNaN100.0mlIntravenous PushNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.79811489M2092-01-24 00:00:00NaNNaNNaN0
4121391488093.001190148809.0240663.02152-05-30 18:00:0030013100.0mlNaNNaN2152-05-30 18:40:0017480.081818764704410NaNNaN100.0mlIntravenous PushNaNNaNNaN148809240663carevueMICUMICU52522152-05-25 23:06:092152-05-31 18:15:145.79811489M2092-01-24 00:00:00NaNNaNNaN0
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" ], "text/plain": [ " SUBJECT_ID HADM_ID_x SEQ_NUM ICD9_CODE HADM_ID_y ICUSTAY_ID_x \\\n", "0 12139 148809 3.0 01190 148809.0 240663.0 \n", "1 12139 148809 3.0 01190 148809.0 240663.0 \n", "2 12139 148809 3.0 01190 148809.0 240663.0 \n", "3 12139 148809 3.0 01190 148809.0 240663.0 \n", "4 12139 148809 3.0 01190 148809.0 240663.0 \n", "\n", " CHARTTIME ITEMID AMOUNT AMOUNTUOM RATE RATEUOM \\\n", "0 2152-05-26 04:00:00 30056 100.0 ml NaN NaN \n", "1 2152-05-26 08:00:00 30056 100.0 ml NaN NaN \n", "2 2152-05-30 06:00:00 30013 50.0 ml NaN NaN \n", "3 2152-05-30 08:00:00 30013 300.0 ml NaN NaN \n", "4 2152-05-30 18:00:00 30013 100.0 ml NaN NaN \n", "\n", " STORETIME CGID ORDERID LINKORDERID STOPPED NEWBOTTLE \\\n", "0 2152-05-26 03:53:00 15477.0 11668241 11668241 NaN NaN \n", "1 2152-05-26 08:10:00 19150.0 7762335 11668241 NaN NaN \n", "2 2152-05-30 05:44:00 20706.0 1555379 4704410 NaN NaN \n", "3 2152-05-30 08:01:00 16890.0 8942938 4704410 NaN NaN \n", "4 2152-05-30 18:40:00 17480.0 8181876 4704410 NaN NaN \n", "\n", " ORIGINALAMOUNT ORIGINALAMOUNTUOM ORIGINALROUTE ORIGINALRATE \\\n", "0 NaN ml Oral NaN \n", "1 NaN ml Oral NaN \n", "2 100.0 ml Intravenous Push NaN \n", "3 100.0 ml Intravenous Push NaN \n", "4 100.0 ml Intravenous Push NaN \n", "\n", " ORIGINALRATEUOM ORIGINALSITE HADM_ID ICUSTAY_ID_y DBSOURCE FIRST_CAREUNIT \\\n", "0 NaN NaN 148809 240663 carevue MICU \n", "1 NaN NaN 148809 240663 carevue MICU \n", "2 NaN NaN 148809 240663 carevue MICU \n", "3 NaN NaN 148809 240663 carevue MICU \n", "4 NaN NaN 148809 240663 carevue MICU \n", "\n", " LAST_CAREUNIT FIRST_WARDID LAST_WARDID INTIME \\\n", "0 MICU 52 52 2152-05-25 23:06:09 \n", "1 MICU 52 52 2152-05-25 23:06:09 \n", "2 MICU 52 52 2152-05-25 23:06:09 \n", "3 MICU 52 52 2152-05-25 23:06:09 \n", "4 MICU 52 52 2152-05-25 23:06:09 \n", "\n", " OUTTIME LOS ROW_ID GENDER DOB DOD \\\n", "0 2152-05-31 18:15:14 5.798 11489 M 2092-01-24 00:00:00 NaN \n", "1 2152-05-31 18:15:14 5.798 11489 M 2092-01-24 00:00:00 NaN \n", "2 2152-05-31 18:15:14 5.798 11489 M 2092-01-24 00:00:00 NaN \n", "3 2152-05-31 18:15:14 5.798 11489 M 2092-01-24 00:00:00 NaN \n", "4 2152-05-31 18:15:14 5.798 11489 M 2092-01-24 00:00:00 NaN \n", "\n", " DOD_HOSP DOD_SSN EXPIRE_FLAG \n", "0 NaN NaN 0 \n", "1 NaN NaN 0 \n", "2 NaN NaN 0 \n", "3 NaN NaN 0 \n", "4 NaN NaN 0 " ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Take only patients that are in tuberc table\n", "\n", "tpat = pd.merge(ticu, pat, how=\"inner\", on=[\"SUBJECT_ID\"])\n", "tpat.drop_duplicates()\n", "print(tpat.shape)\n", "tpat.head()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Seq icd-9\n", "\n", "p = sns.histplot(data=tpat,x=\"SEQ_NUM\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Number of distinct patients\n", "\n", "import numpy as np\n", "\n", "np.size(tpat.SUBJECT_ID.unique())" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'sns' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtpat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"SUBJECT_ID\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ICUSTAY_ID_x\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined" ] } ], "source": [ "p = sns.histplot(data=tpat,x=\"SUBJECT_ID\", y=\"ICUSTAY_ID_x\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 4 }