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pregnant.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 17,
"id": "3f87254e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last modified by Xiaoqing: 211210\n"
]
}
],
"source": [
"from datetime import datetime\n",
"date = datetime.today().strftime('%y%m%d')\n",
"print ('Last modified by Xiaoqing: ' + date)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "6114c11a",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "d3e710b5",
"metadata": {},
"source": [
"# Problem statement\n",
"Words such as 'pregnant,' 'breast-feeding,' and 'lactating' frequently appear in clinical trial eligibility. Unfortunately they are not recognized by stanza NER (stanza can however, recognize 'pregnancy' as a problem). \n",
"\n",
"Let's fix this."
]
},
{
"cell_type": "markdown",
"id": "86e4c32b",
"metadata": {},
"source": [
"# Note:\n",
" For this notebook to work, all key words must be extracted from a clinical trial. \n",
" \n",
" One clinical trial can have multiple rows; each row corresponds to a different key word.\n",
" \n",
" Under criteria, we should have ALL the bullet points, without separting them into different rows."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "49cd1778",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('input_pregnant_121021.csv')\n",
"df['criteria']= df['criteria'].str.lower()\n",
"df['key_words']= df['key_words'].str.lower()"
]
},
{
"cell_type": "markdown",
"id": "cb993e1c",
"metadata": {},
"source": [
"# The less common scenarios\n",
"In the less common scenarios, for example, a clinical trial may want to study pregnancy related diabetes. These studies DO want to recruit pregnant women."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c7a7d7d8",
"metadata": {},
"outputs": [],
"source": [
"# Does a study want to recruit pregnant women? \n",
"# If pregnant = 1, it means they want to INCLUDE pregnant women.\n",
"# If pregnant = 0, it means they want to EXCLUDE pregnant women.\n",
"\n",
"df['pregnant'] = np.nan\n",
"\n",
"for index, row in df.iterrows():\n",
" if 'pregnant' in row['key_words'] or 'pregnancy' in row['key_words']:\n",
" df.loc[index,'pregnant'] = 1\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "903e2281",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>criteria</th>\n",
" <th>key_words</th>\n",
" <th>pregnant</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>sedentary time</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>pregnancy</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>pregnant women</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>weight gain</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>15</td>\n",
" <td>this is a study that does not mention anything...</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id criteria key_words \\\n",
"16 14 women pregnant with one fetus between 16 and 2... sedentary time \n",
"17 14 women pregnant with one fetus between 16 and 2... pregnancy \n",
"18 14 women pregnant with one fetus between 16 and 2... pregnant women \n",
"19 14 women pregnant with one fetus between 16 and 2... weight gain \n",
"20 15 this is a study that does not mention anything... testing \n",
"\n",
" pregnant \n",
"16 NaN \n",
"17 1.0 \n",
"18 1.0 \n",
"19 NaN \n",
"20 NaN "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "14d19db1",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>pregnant</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>10</th>\n",
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"text/plain": [
" id pregnant\n",
"10 11 NaN\n",
"11 12 NaN\n",
"12 13 NaN\n",
"13 14 1.0\n",
"14 15 NaN"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grouping by id, if one of the key words contain Pregnancy related words, we label that entire study as pregnant = 1 \n",
"df1 = df.groupby(['id'])['pregnant'].agg('max').reset_index()\n",
"df1.tail()\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "3f32dd16",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/n3/x7vymc8s2cj1p5yx96j08wlr0000gn/T/ipykernel_1469/2839200337.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
" df = df.drop('pregnant', 1)\n"
]
}
],
"source": [
"# now merge this with the long format df\n",
"df = df.drop('pregnant', 1)\n",
"df2 = df.merge(df1, on='id', how='outer')\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "4bcb95f1",
"metadata": {},
"outputs": [
{
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" <th>3</th>\n",
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" <tr>\n",
" <th>4</th>\n",
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" <td>pregnant women or women currently breastfeeding;</td>\n",
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" <th>5</th>\n",
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" <td>females who are pregnant or nursing</td>\n",
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" <tr>\n",
" <th>6</th>\n",
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" <td>in the case of women of childbearing age, urin...</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>6</td>\n",
" <td>are pregnant or lactating or planning to becom...</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>7</td>\n",
" <td>pregnant or breast-feeding</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>8</td>\n",
" <td>patients who are pregnant or may be pregnant</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>9</td>\n",
" <td>not pregnant or nursing</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10</td>\n",
" <td>females who are pregnant.</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11</td>\n",
" <td>for females of child-bearing age, current preg...</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>12</td>\n",
" <td>for females of child-bearing age, current brea...</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>13</td>\n",
" <td>are not pregnant (negative urine test) or brea...</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>physical activity</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>sedentary time</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>pregnancy</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>pregnant women</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>weight gain</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>15</td>\n",
" <td>this is a study that does not mention anything...</td>\n",
" <td>testing</td>\n",
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],
"text/plain": [
" id criteria key_words \\\n",
"0 1 for female participants, currently breastfeedi... testing \n",
"1 1 for female participants, currently breastfeedi... testing \n",
"2 1 for female participants, currently breastfeedi... testing \n",
"3 2 patients will be excluded if they are pregnant. testing \n",
"4 3 pregnant women or women currently breastfeeding; testing \n",
"5 4 females who are pregnant or nursing testing \n",
"6 5 in the case of women of childbearing age, urin... testing \n",
"7 6 are pregnant or lactating or planning to becom... testing \n",
"8 7 pregnant or breast-feeding testing \n",
"9 8 patients who are pregnant or may be pregnant testing \n",
"10 9 not pregnant or nursing testing \n",
"11 10 females who are pregnant. testing \n",
"12 11 for females of child-bearing age, current preg... testing \n",
"13 12 for females of child-bearing age, current brea... testing \n",
"14 13 are not pregnant (negative urine test) or brea... testing \n",
"15 14 women pregnant with one fetus between 16 and 2... physical activity \n",
"16 14 women pregnant with one fetus between 16 and 2... sedentary time \n",
"17 14 women pregnant with one fetus between 16 and 2... pregnancy \n",
"18 14 women pregnant with one fetus between 16 and 2... pregnant women \n",
"19 14 women pregnant with one fetus between 16 and 2... weight gain \n",
"20 15 this is a study that does not mention anything... testing \n",
"\n",
" pregnant \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 1.0 \n",
"16 1.0 \n",
"17 1.0 \n",
"18 1.0 \n",
"19 1.0 \n",
"20 NaN "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "markdown",
"id": "a15339fb",
"metadata": {},
"source": [
"# Most common scenarios"
]
},
{
"cell_type": "markdown",
"id": "d8aa02c4",
"metadata": {},
"source": [
"In the most common scenario, clinical trials do not want to recruit women who are pregnant or breast-feeding, out of concern for the baby.\n",
"\n",
"If a clinical trial's key words do not contain pregnancy related words AND the study eligibility mentioned pregnancy related words, we will mark them as a study that wants to AVOID recruiting pregnant women."
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "ca59627d",
"metadata": {},
"outputs": [
{
"data": {
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" <th>8</th>\n",
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" <td>testing</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>8</td>\n",
" <td>patients who are pregnant or may be pregnant</td>\n",
" <td>testing</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>9</td>\n",
" <td>not pregnant or nursing</td>\n",
" <td>testing</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10</td>\n",
" <td>females who are pregnant.</td>\n",
" <td>testing</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11</td>\n",
" <td>for females of child-bearing age, current preg...</td>\n",
" <td>testing</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>12</td>\n",
" <td>for females of child-bearing age, current brea...</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>13</td>\n",
" <td>are not pregnant (negative urine test) or brea...</td>\n",
" <td>testing</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>physical activity</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>sedentary time</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>pregnancy</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>pregnant women</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>14</td>\n",
" <td>women pregnant with one fetus between 16 and 2...</td>\n",
" <td>weight gain</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>15</td>\n",
" <td>this is a study that does not mention anything...</td>\n",
" <td>testing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id criteria key_words \\\n",
"0 1 for female participants, currently breastfeedi... testing \n",
"1 1 for female participants, currently breastfeedi... testing \n",
"2 1 for female participants, currently breastfeedi... testing \n",
"3 2 patients will be excluded if they are pregnant. testing \n",
"4 3 pregnant women or women currently breastfeeding; testing \n",
"5 4 females who are pregnant or nursing testing \n",
"6 5 in the case of women of childbearing age, urin... testing \n",
"7 6 are pregnant or lactating or planning to becom... testing \n",
"8 7 pregnant or breast-feeding testing \n",
"9 8 patients who are pregnant or may be pregnant testing \n",
"10 9 not pregnant or nursing testing \n",
"11 10 females who are pregnant. testing \n",
"12 11 for females of child-bearing age, current preg... testing \n",
"13 12 for females of child-bearing age, current brea... testing \n",
"14 13 are not pregnant (negative urine test) or brea... testing \n",
"15 14 women pregnant with one fetus between 16 and 2... physical activity \n",
"16 14 women pregnant with one fetus between 16 and 2... sedentary time \n",
"17 14 women pregnant with one fetus between 16 and 2... pregnancy \n",
"18 14 women pregnant with one fetus between 16 and 2... pregnant women \n",
"19 14 women pregnant with one fetus between 16 and 2... weight gain \n",
"20 15 this is a study that does not mention anything... testing \n",
"\n",
" pregnant lactating \n",
"0 NaN 0.0 \n",
"1 NaN 0.0 \n",
"2 NaN 0.0 \n",
"3 0.0 NaN \n",
"4 0.0 0.0 \n",
"5 0.0 0.0 \n",
"6 0.0 NaN \n",
"7 0.0 0.0 \n",
"8 0.0 0.0 \n",
"9 0.0 NaN \n",
"10 0.0 0.0 \n",
"11 0.0 NaN \n",
"12 0.0 NaN \n",
"13 NaN 0.0 \n",
"14 0.0 0.0 \n",
"15 1.0 NaN \n",
"16 1.0 NaN \n",
"17 1.0 NaN \n",
"18 1.0 NaN \n",
"19 1.0 NaN \n",
"20 NaN NaN "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2['lactating'] = np.nan\n",
"\n",
"for index, row in df2.iterrows():\n",
" if row['pregnant'] != 1:\n",
" if 'pregnant' in row['criteria'] or 'pregnancy' in row['criteria']:\n",
" df2.loc[index,'pregnant'] = 0\n",
" if 'nursing' in row['criteria'] or 'breast-feeding' in row['criteria'] or 'breastfeeding' in row['criteria'] or 'breast feeding' in row['criteria'] or 'lactating' in row['criteria']:\n",
" df2.loc[index,'lactating'] = 0\n",
" \n",
"df2\n",
" "
]
},
{
"cell_type": "markdown",
"id": "a62e65a9",
"metadata": {},
"source": [
"Now we see that for each study we are indicating whether they want to...\n",
"- exclude women who are pregnant (pregnant = 0)\n",
"- include women who are pregnant (pregnant = 1)\n",
"- exclude women who are lactating (lactating = 0)\n",
"- or they did not specify whether they care about pregnancy (NaN)\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "54038d07",
"metadata": {},
"outputs": [],
"source": [
"df2.to_csv(('output_pregnant_'+ date + '.csv'),index = False)"
]
},
{
"cell_type": "markdown",
"id": "240c04d5",
"metadata": {},
"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": "3.8.12"
}
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