Downloads: 1
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "9462896c-2450-43da-9fe5-3c9f14c93cc6",
"metadata": {},
"outputs": [],
"source": [
"# IMPORT DEPENDENCIES\n",
"# Import dependencies\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "317f1a4e-18ca-4c3c-9eb7-10af3f963822",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>OrgStudyId</th>\n",
" <th>WhyStopped</th>\n",
" <th>EnrollmentCount</th>\n",
" <th>PrimaryOutcomeMeasure</th>\n",
" <th>FlowDropWithdrawType</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>BTX-BCI-016-PRT</td>\n",
" <td>NaN</td>\n",
" <td>3000</td>\n",
" <td>To determine BCI test performance by evaluatin...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2018-TJ-BCD</td>\n",
" <td>NaN</td>\n",
" <td>2300</td>\n",
" <td>Diagnostic potential of SEMA4C as a biomarker ...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Breast cancer</td>\n",
" <td>NaN</td>\n",
" <td>80</td>\n",
" <td>Role of SORCIN in patients with breast cancer</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>BC-BOMET</td>\n",
" <td>NaN</td>\n",
" <td>30</td>\n",
" <td>SENP1 expression</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>241391</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" <td>Performance of the Syantra DX Breast Cancer te...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>IL-TM-B1-01</td>\n",
" <td>NaN</td>\n",
" <td>200</td>\n",
" <td>This study is intended to evaluate the sensiti...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>FH-Risk 2.0 Research Protocol</td>\n",
" <td>NaN</td>\n",
" <td>271</td>\n",
" <td>To explore how much new risk models change bre...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>ID-RPSBC-01-20201012</td>\n",
" <td>NaN</td>\n",
" <td>316</td>\n",
" <td>Absolute risk difference between breast cancer...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>IRST174.22</td>\n",
" <td>NaN</td>\n",
" <td>60000</td>\n",
" <td>To compare the cumulative incidence of stage 2...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>ANILERGİNN</td>\n",
" <td>NaN</td>\n",
" <td>300</td>\n",
" <td>breast cancer incidence after laparoscopic sle...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank OrgStudyId WhyStopped EnrollmentCount \\\n",
"0 1 BTX-BCI-016-PRT NaN 3000 \n",
"1 2 2018-TJ-BCD NaN 2300 \n",
"2 3 Breast cancer NaN 80 \n",
"3 4 BC-BOMET NaN 30 \n",
"4 5 241391 NaN 600 \n",
"5 6 IL-TM-B1-01 NaN 200 \n",
"6 7 FH-Risk 2.0 Research Protocol NaN 271 \n",
"7 8 ID-RPSBC-01-20201012 NaN 316 \n",
"8 9 IRST174.22 NaN 60000 \n",
"9 10 ANILERGİNN NaN 300 \n",
"\n",
" PrimaryOutcomeMeasure FlowDropWithdrawType \n",
"0 To determine BCI test performance by evaluatin... NaN \n",
"1 Diagnostic potential of SEMA4C as a biomarker ... NaN \n",
"2 Role of SORCIN in patients with breast cancer NaN \n",
"3 SENP1 expression NaN \n",
"4 Performance of the Syantra DX Breast Cancer te... NaN \n",
"5 This study is intended to evaluate the sensiti... NaN \n",
"6 To explore how much new risk models change bre... NaN \n",
"7 Absolute risk difference between breast cancer... NaN \n",
"8 To compare the cumulative incidence of stage 2... NaN \n",
"9 breast cancer incidence after laparoscopic sle... NaN "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# READ IN FILE\n",
"df = pd.read_csv('Tables/free_text_df.csv')\n",
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4537506e-12ad-4d8b-bb6f-8a76d97ae37e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5013 entries, 0 to 5012\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Rank 5013 non-null int64 \n",
" 1 OrgStudyId 5013 non-null object\n",
" 2 WhyStopped 320 non-null object\n",
" 3 EnrollmentCount 5013 non-null int64 \n",
" 4 PrimaryOutcomeMeasure 5013 non-null object\n",
" 5 FlowDropWithdrawType 70 non-null object\n",
"dtypes: int64(2), object(4)\n",
"memory usage: 235.1+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d5649ff7-a0d0-4737-bd7e-7d9d0f14ae92",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" }\n",
"\n",
" .dataframe thead th {\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>WhyStopped</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Pandemic situation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Technical problem with plasma blood samples ob...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>study did not start and is currently on pause</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Principal investigator left the study institut...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>The study was stopped prematurely due to insuf...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4967</th>\n",
" <td>Technical problem with blood plasma samples ob...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4968</th>\n",
" <td>Evolving data with Ipatasertib that changes th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4970</th>\n",
" <td>Study is part of PhD trajectory and currently ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4981</th>\n",
" <td>no funding</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4986</th>\n",
" <td>sponsor on campus training restrictions</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>320 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" WhyStopped\n",
"32 Pandemic situation\n",
"47 Technical problem with plasma blood samples ob...\n",
"50 study did not start and is currently on pause\n",
"54 Principal investigator left the study institut...\n",
"84 The study was stopped prematurely due to insuf...\n",
"... ...\n",
"4967 Technical problem with blood plasma samples ob...\n",
"4968 Evolving data with Ipatasertib that changes th...\n",
"4970 Study is part of PhD trajectory and currently ...\n",
"4981 no funding\n",
"4986 sponsor on campus training restrictions\n",
"\n",
"[320 rows x 1 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Extract WhyStopped column and drop null values\n",
"df_text = pd.DataFrame(df['WhyStopped'])\n",
"df_text = df_text.dropna()\n",
"df_text"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "08352273-ac09-41d5-9c43-7d2ecc3438d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 320 entries, 32 to 4986\n",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 WhyStopped 320 non-null object\n",
"dtypes: object(1)\n",
"memory usage: 5.0+ KB\n"
]
}
],
"source": [
"df_text.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "74b3827b-a463-43a1-bf77-3c2c677f4fb1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>WhyStopped</th>\n",
" <th>word_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Pandemic situation</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Technical problem with plasma blood samples ob...</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>study did not start and is currently on pause</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Principal investigator left the study institut...</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>The study was stopped prematurely due to insuf...</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>No participants enrolled</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>PI no longer working at Indiana University;</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>Temporarily paused per study team for interim ...</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>Study classified as out of scope by the Ethics...</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>Enrollment into AWARE cohorts1-4 have conclude...</td>\n",
" <td>18</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WhyStopped word_count\n",
"32 Pandemic situation 2\n",
"47 Technical problem with plasma blood samples ob... 10\n",
"50 study did not start and is currently on pause 9\n",
"54 Principal investigator left the study institut... 6\n",
"84 The study was stopped prematurely due to insuf... 9\n",
"97 No participants enrolled 3\n",
"105 PI no longer working at Indiana University; 7\n",
"112 Temporarily paused per study team for interim ... 9\n",
"131 Study classified as out of scope by the Ethics... 16\n",
"143 Enrollment into AWARE cohorts1-4 have conclude... 18"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_text['word_count'] = df_text['WhyStopped'].apply(lambda x: len(str(x).split(\" \")))\n",
"df_text.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4f0097e7-54f2-4414-810a-255de0258b90",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9.0625"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# AVERAGE WORD COUNT\n",
"df_text['word_count'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "415583c8-07e1-4a8d-ae1a-bb692524736a",
"metadata": {},
"outputs": [],
"source": [
"# IMPORT STOPWORD LIBRARY\n",
"from nltk.corpus import stopwords\n",
"stop = stopwords.words('english')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "dcd05c97-aef0-449f-b72c-7a8feebf5cf5",
"metadata": {},
"outputs": [
{
"data": {
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" <td>Pandemic situation</td>\n",
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" <tr>\n",
" <th>47</th>\n",
" <td>Technical problem with plasma blood samples ob...</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>study did not start and is currently on pause</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Principal investigator left the study institut...</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>The study was stopped prematurely due to insuf...</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>No participants enrolled</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>PI no longer working at Indiana University;</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>Temporarily paused per study team for interim ...</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>Study classified as out of scope by the Ethics...</td>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>Enrollment into AWARE cohorts1-4 have conclude...</td>\n",
" <td>18</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WhyStopped word_count stop_words\n",
"32 Pandemic situation 2 0\n",
"47 Technical problem with plasma blood samples ob... 10 3\n",
"50 study did not start and is currently on pause 9 5\n",
"54 Principal investigator left the study institut... 6 1\n",
"84 The study was stopped prematurely due to insuf... 9 2\n",
"97 No participants enrolled 3 0\n",
"105 PI no longer working at Indiana University; 7 2\n",
"112 Temporarily paused per study team for interim ... 9 1\n",
"131 Study classified as out of scope by the Ethics... 16 6\n",
"143 Enrollment into AWARE cohorts1-4 have conclude... 18 8"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_text['stop_words'] = df_text['WhyStopped'].apply(\n",
" lambda x: len([x for x in x.split() if x in stop]))\n",
"df_text.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "79e0a2ea-f1ff-474f-84e3-85c382edbce9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.95625"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# AVERAGE STOPWORDS\n",
"df_text['stop_words'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b6e68297-622f-4e40-ba0e-e1ce4fc0101e",
"metadata": {},
"outputs": [
{
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" <th>97</th>\n",
" <td>no participants enrolled</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>105</th>\n",
" <td>pi no longer working at indiana university;</td>\n",
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" <tr>\n",
" <th>143</th>\n",
" <td>enrollment into aware cohorts1-4 have conclude...</td>\n",
" <td>18</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WhyStopped word_count stop_words\n",
"32 pandemic situation 2 0\n",
"47 technical problem with plasma blood samples ob... 10 3\n",
"50 study did not start and is currently on pause 9 5\n",
"54 principal investigator left the study institut... 6 1\n",
"84 the study was stopped prematurely due to insuf... 9 2\n",
"97 no participants enrolled 3 0\n",
"105 pi no longer working at indiana university; 7 2\n",
"112 temporarily paused per study team for interim ... 9 1\n",
"131 study classified as out of scope by the ethics... 16 6\n",
"143 enrollment into aware cohorts1-4 have conclude... 18 8"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# LOWERCASE RESPONSES\n",
"df_text['WhyStopped'] = df_text['WhyStopped'].apply(\n",
" lambda x: \" \".join(x.lower() for x in x.split()))\n",
"df_text.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b37154ec-5526-48ae-95bc-8432591e24fa",
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9b6968c9-1515-4a5f-ae26-d44b8604e197",
"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>WhyStopped</th>\n",
" <th>word_count</th>\n",
" <th>stop_words</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>pandemic situation</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>technical problem with plasma blood samples ob...</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>study did not start and is currently on pause</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>principal investigator left the study institution</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>the study was stopped prematurely due to insuf...</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>no participants enrolled</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>pi no longer working at indiana university</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>temporarily paused per study team for interim ...</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>study classified as out of scope by the ethics...</td>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>enrollment into aware cohorts14 have concluded...</td>\n",
" <td>18</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WhyStopped word_count stop_words\n",
"32 pandemic situation 2 0\n",
"47 technical problem with plasma blood samples ob... 10 3\n",
"50 study did not start and is currently on pause 9 5\n",
"54 principal investigator left the study institution 6 1\n",
"84 the study was stopped prematurely due to insuf... 9 2\n",
"97 no participants enrolled 3 0\n",
"105 pi no longer working at indiana university 7 2\n",
"112 temporarily paused per study team for interim ... 9 1\n",
"131 study classified as out of scope by the ethics... 16 6\n",
"143 enrollment into aware cohorts14 have concluded... 18 8"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# REMOVING PUNCTUATION\n",
"df_text['WhyStopped'] = df_text['WhyStopped'].str.replace('[^\\w\\s]','')\n",
"df_text.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "02bf2432-7098-472f-8f2c-ed9fa60c1068",
"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>WhyStopped</th>\n",
" <th>word_count</th>\n",
" <th>stop_words</th>\n",
" <th>filtered_responses</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>pandemic situation</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>pandemic situation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>technical problem with plasma blood samples ob...</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>technical problem plasma blood samples obtaine...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>study did not start and is currently on pause</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>study start currently pause</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>principal investigator left the study institution</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>principal investigator left study institution</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>the study was stopped prematurely due to insuf...</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>study stopped prematurely due insufficient rec...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>no participants enrolled</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>participants enrolled</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>pi no longer working at indiana university</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>pi longer working indiana university</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>temporarily paused per study team for interim ...</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>temporarily paused per study team interim data...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>study classified as out of scope by the ethics...</td>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>study classified scope ethics committee projec...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>enrollment into aware cohorts14 have concluded...</td>\n",
" <td>18</td>\n",
" <td>8</td>\n",
" <td>enrollment aware cohorts14 concluded primary o...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WhyStopped word_count \\\n",
"32 pandemic situation 2 \n",
"47 technical problem with plasma blood samples ob... 10 \n",
"50 study did not start and is currently on pause 9 \n",
"54 principal investigator left the study institution 6 \n",
"84 the study was stopped prematurely due to insuf... 9 \n",
"97 no participants enrolled 3 \n",
"105 pi no longer working at indiana university 7 \n",
"112 temporarily paused per study team for interim ... 9 \n",
"131 study classified as out of scope by the ethics... 16 \n",
"143 enrollment into aware cohorts14 have concluded... 18 \n",
"\n",
" stop_words filtered_responses \n",
"32 0 pandemic situation \n",
"47 3 technical problem plasma blood samples obtaine... \n",
"50 5 study start currently pause \n",
"54 1 principal investigator left study institution \n",
"84 2 study stopped prematurely due insufficient rec... \n",
"97 0 participants enrolled \n",
"105 2 pi longer working indiana university \n",
"112 1 temporarily paused per study team interim data... \n",
"131 6 study classified scope ethics committee projec... \n",
"143 8 enrollment aware cohorts14 concluded primary o... "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# REMOVE STPWORDS\n",
"df_text['filtered_responses'] = df_text['WhyStopped'].apply(\n",
" lambda x: \" \".join(x for x in x.split() if x not in stop))\n",
"df_text.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "21937e27-2108-48df-b7de-944ebf169ec0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"study 112\n",
"due 76\n",
"decision 35\n",
"dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# WORD FREQUENCY\n",
"freq = pd.Series(' '.join(df_text['filtered_responses']).split()).value_counts()[:3]\n",
"freq"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "25a44150-7257-4785-b34f-98ba3389715e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"32 pandemic situation\n",
"47 technical problem plasma blood samples obtaine...\n",
"50 start currently pause\n",
"54 principal investigator left institution\n",
"84 stopped prematurely insufficient recruitment\n",
"Name: filtered_responses, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# FILTERING OUT TOP THREE MOST FREQUENT WORDS\n",
"freq = list(freq.index)\n",
"df_text['filtered_responses'] = df_text['filtered_responses'].apply(\n",
" lambda x: \" \".join(x for x in x.split() if x not in freq))\n",
"df_text['filtered_responses'].head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "7e95e955-a506-4f40-9ae2-b8f4486d5655",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sponsor 30\n",
"funding 27\n",
"enrollment 25\n",
"accrual 25\n",
"recruitment 24\n",
"covid19 23\n",
"terminated 22\n",
"safety 21\n",
"patients 20\n",
"trial 19\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_freq = pd.Series(' '.join(df_text['filtered_responses']).split()).value_counts()[:10]\n",
"new_freq"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c48a1b42-3488-4e30-bdc6-7ad348cb5cd6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"new_freq.plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c2cee97-c154-4bbf-b59a-d26216ecb0bb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e4b2da91-9887-4d4c-b26e-4627479f6e9a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7d432db9-e432-4edb-8d6a-e7e17bf98da5",
"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>WhyStopped</th>\n",
" <th>word_count</th>\n",
" <th>stop_words</th>\n",
" <th>filtered_responses</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3413</th>\n",
" <td>sponsor decision</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>sponsor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3315</th>\n",
" <td>funding sponsor filing of chapter 11 bankruptcy</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>funding sponsor filing chapter 11 bankruptcy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4127</th>\n",
" <td>sponsor decision based on strategic realignment</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>sponsor based strategic realignment</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1897</th>\n",
" <td>decision to discontinue the study based on bro...</td>\n",
" <td>24</td>\n",
" <td>9</td>\n",
" <td>discontinue based broader development strategi...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2570</th>\n",
" <td>this was a sponsor decision and was not a cons...</td>\n",
" <td>14</td>\n",
" <td>8</td>\n",
" <td>sponsor consequence safety concern</td>\n",
" </tr>\n",
" <tr>\n",
" <th>896</th>\n",
" <td>after demonstrating the on target effect of gm...</td>\n",
" <td>23</td>\n",
" <td>6</td>\n",
" <td>demonstrating target effect gmi1359 via pharma...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4221</th>\n",
" <td>sponsor decision based on strategic realignment</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>sponsor based strategic realignment</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2267</th>\n",
" <td>administrative closure based on sponsor recomm...</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>administrative closure based sponsor recommend...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4610</th>\n",
" <td>based on the overall results from the phase 1 ...</td>\n",
" <td>28</td>\n",
" <td>12</td>\n",
" <td>based overall results phase 1 part sponsor dec...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1242</th>\n",
" <td>terminated by sponsor due to lack of interest</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>terminated sponsor lack interest</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1921</th>\n",
" <td>the study was prematurely discontinued by the ...</td>\n",
" <td>23</td>\n",
" <td>11</td>\n",
" <td>prematurely discontinued sponsor probability s...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2030</th>\n",
" <td>due to the fact that the sponsor decided not t...</td>\n",
" <td>17</td>\n",
" <td>9</td>\n",
" <td>fact sponsor decided move forward development ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3117</th>\n",
" <td>sponsor decision not safety related</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>sponsor safety related</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1695</th>\n",
" <td>sponsor withdrew support</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>sponsor withdrew support</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4392</th>\n",
" <td>the sponsor terminated study after dosing 2 do...</td>\n",
" <td>36</td>\n",
" <td>9</td>\n",
" <td>sponsor terminated dosing 2 dose groups 7 pts ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2292</th>\n",
" <td>sponsors decision no safety concerns</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>sponsors safety concerns</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2075</th>\n",
" <td>per sponsor</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>per sponsor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3113</th>\n",
" <td>the study was terminated due to the review of ...</td>\n",
" <td>26</td>\n",
" <td>11</td>\n",
" <td>terminated review asset vbir2 within sponsors ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2711</th>\n",
" <td>the sponsor has discontinued the development o...</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>sponsor discontinued development tesetaxel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2474</th>\n",
" <td>sponsor decision</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>sponsor</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WhyStopped word_count \\\n",
"3413 sponsor decision 2 \n",
"3315 funding sponsor filing of chapter 11 bankruptcy 8 \n",
"4127 sponsor decision based on strategic realignment 6 \n",
"1897 decision to discontinue the study based on bro... 24 \n",
"2570 this was a sponsor decision and was not a cons... 14 \n",
"896 after demonstrating the on target effect of gm... 23 \n",
"4221 sponsor decision based on strategic realignment 6 \n",
"2267 administrative closure based on sponsor recomm... 10 \n",
"4610 based on the overall results from the phase 1 ... 28 \n",
"1242 terminated by sponsor due to lack of interest 8 \n",
"1921 the study was prematurely discontinued by the ... 23 \n",
"2030 due to the fact that the sponsor decided not t... 17 \n",
"3117 sponsor decision not safety related 5 \n",
"1695 sponsor withdrew support 3 \n",
"4392 the sponsor terminated study after dosing 2 do... 36 \n",
"2292 sponsors decision no safety concerns 5 \n",
"2075 per sponsor 2 \n",
"3113 the study was terminated due to the review of ... 26 \n",
"2711 the sponsor has discontinued the development o... 8 \n",
"2474 sponsor decision 2 \n",
"\n",
" stop_words filtered_responses \n",
"3413 0 sponsor \n",
"3315 1 funding sponsor filing chapter 11 bankruptcy \n",
"4127 1 sponsor based strategic realignment \n",
"1897 9 discontinue based broader development strategi... \n",
"2570 8 sponsor consequence safety concern \n",
"896 6 demonstrating target effect gmi1359 via pharma... \n",
"4221 1 sponsor based strategic realignment \n",
"2267 2 administrative closure based sponsor recommend... \n",
"4610 12 based overall results phase 1 part sponsor dec... \n",
"1242 3 terminated sponsor lack interest \n",
"1921 11 prematurely discontinued sponsor probability s... \n",
"2030 9 fact sponsor decided move forward development ... \n",
"3117 1 sponsor safety related \n",
"1695 0 sponsor withdrew support \n",
"4392 9 sponsor terminated dosing 2 dose groups 7 pts ... \n",
"2292 1 sponsors safety concerns \n",
"2075 0 per sponsor \n",
"3113 11 terminated review asset vbir2 within sponsors ... \n",
"2711 3 sponsor discontinued development tesetaxel \n",
"2474 0 sponsor "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rate_freq = df_text.loc[df_text['WhyStopped'].str.contains(\"sponsor\", case=False)]\n",
"rate_freq.sample(20)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "06cac714-6419-4231-9c26-0c7f7beaa2a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9983 confirmed objective response rate by recist 11...\n",
"9984 level of decisional conflict level of decision...\n",
"9985 cancerrelated fatigue severity as assessed usi...\n",
"9986 detection rate of sentinel node technique\n",
"9987 risk of reporting cancer overall specific canc...\n",
"9988 time until adverse liver outcome assessed by i...\n",
"9989 the feasibility of hpv testing as a single vis...\n",
"9990 genomics outcome scale\n",
"9991 fatigue severity impact of fatigue state of fa...\n",
"9992 the role of vitamin d in response to neoadjuva...\n",
"9993 rate of men diagnosed with pc and aggressive p...\n",
"9994 doubling of progression free survival\n",
"9995 objective response rate orr\n",
"9996 icg transit time detected by the smartgoggles ...\n",
"9997 number of participants with grade 2 radiation ...\n",
"9998 the number of circulating tumor cells\n",
"9999 to evaluate the impact of visceral adipose tis...\n",
"10000 feasibility defined by percent randomized numb...\n",
"10001 number of patients with complete data safe\n",
"10002 the psychosocial factors predictive of uptake ...\n",
"10003 overall survival os\n",
"10004 occult cancer missed by screening strategies\n",
"10005 the role of magnetic resonance imaging mri in ...\n",
"10006 objective response or durable clinical benefit...\n",
"10007 change in mdasi scores incidents of dehydration\n",
"10008 the primary outcome of the study will be durat...\n",
"10009 overall survival\n",
"10010 psa response rate\n",
"10011 3year local recurrence rate\n",
"10012 initially targeted drug therapy risks in patie...\n",
"Name: WhyStopped, dtype: object"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_text['WhyStopped'].tail(30)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "2650179f-de07-497b-abd7-54fd53a882a8",
"metadata": {},
"outputs": [],
"source": [
"# IMPORT WORDCLOUD LIBRARY\n",
"from wordcloud import WordCloud"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "526319e9-c2b9-480b-bbbf-9dcffb5d6841",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=400x200>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# GENERATE A WORD CLOUD OF FILTERED RESPONSES\n",
"long_string = ','.join(list(df_text['filtered_responses'].values))\n",
"\n",
"wordcloud = WordCloud(\n",
" background_color=\"white\",\n",
" max_words = 5000,\n",
" contour_width=3,\n",
" contour_color='steelblue')\n",
"\n",
"wordcloud.generate(long_string)\n",
"\n",
"wordcloud.to_image()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "c9d4476d-229e-4412-a701-49dfdca5e03a",
"metadata": {},
"outputs": [],
"source": [
"# IMPORT GENSIM FOR LATENT DIRILECHT ALLOCATION\n",
"import gensim\n",
"from gensim import corpora\n",
"from gensim.utils import simple_preprocess"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "fe4c7e08-2d4d-4310-a949-f697cd88170d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['pandemic', 'situation'], ['technical', 'problem', 'plasma', 'blood', 'samples', 'obtained', 'patients'], ['start', 'currently', 'pause'], ['principal', 'investigator', 'left', 'institution'], ['stopped', 'prematurely', 'insufficient', 'recruitment'], ['participants', 'enrolled'], ['pi', 'longer', 'working', 'indiana', 'university'], ['temporarily', 'paused', 'per', 'team', 'interim', 'data', 'review'], ['classified', 'scope', 'ethics', 'committee', 'project', 'involving', 'human', 'person'], ['enrollment', 'aware', 'cohorts', 'concluded', 'primary', 'objective', 'core', 'goals', 'met'], ['slow', 'recruitment', 'rate'], ['sponsor', 'prematurely', 'stop', 'linked', 'safety', 'concern'], ['one', 'participant', 'accrued', 'stopped', 'new', 'safety', 'data', 'company', 'slow', 'accrual'], ['researcher', 'able', 'recruit', 'patients', 'abandoned', 'project'], ['unable', 'achieve', 'devices', 'used'], ['moving', 'different', 'institution', 'pi'], ['decided', 'halt', 'potentially', 'reopen', 'future'], ['covid', 'trial', 'never', 'got', 'running'], ['halted', 'prematurely', 'low', 'recruitment'], ['participant', 'recruitment', 'stopped', 'corona', 'pandemic'], ['poor', 'enrolled', 'patients'], ['termination', 'collaboration', 'puma'], ['terminated', 'based', 'pfizers', 'change', 'clinical', 'development', 'strategy', 'related', 'safety', 'efficacy'], ['clinical', 'treatment', 'patients', 'stage', 'iv', 'breast', 'cancer', 'received', 'surgical', 'treatment', 'made', 'difficult', 'enroll', 'project', 'terminated'], ['logistics'], ['prematurely', 'discontinued', 'significant', 'data', 'quality', 'issues', 'august', 'safety', 'concerns', 'led', 'terminate'], ['recruitment', 'difficulties'], ['lack', 'accrual'], ['covid', 'pandemic', 'pursuing'], ['pi', 'moving', 'new', 'institution'], ['institutional', 'conflict', 'interest'], ['approved', 'treatment', 'regimen', 'based', 'current', 'guidelines', 'however', 'reimbursement', 'imp', 'feasible'], ['surgery', 'wait', 'times', 'covid', 'pandemic', 'eventually', 'reduced', 'prepandemic', 'wait', 'times', 'became', 'irrelevant'], ['withdrawn', 'change', 'plan', 'safety', 'reasons'], ['limited', 'staff', 'carry'], ['strategic', 'changes', 'regarding', 'product', 'development'], ['premature', 'termination', 'lack', 'funding'], ['security', 'effect', 'data', 'another', 'ongoing'], ['relocation', 'principal', 'investigator'], ['covid', 'never', 'started'], ['dose', 'limiting', 'toxicity'], ['insurance', 'nonpayment', 'subjects', 'enrolled'], ['enrollment'], ['demonstrating', 'target', 'effect', 'gmi', 'via', 'pharmacodynamic', 'markers', 'cxcr', 'eselectin', 'sponsor', 'terminated', 'trial', 'covidrelated', 'slow', 'enrollment'], ['treatment', 'standard', 'changed'], ['strategic', 'considerations'], ['terminated', 'part', 'dose', 'escalation', 'part', 'expansion', 'initiated', 'voluntarily', 'terminated', 'business', 'proceed', 'isb', 'asset', 'safety', 'issue'], ['initiating', 'new', 'revised', 'statistics'], ['inadequate', 'fna', 'samples', 'low', 'cell', 'counts'], ['difficulty', 'accruing', 'subjects', 'accrual', 'closed'], ['funder'], ['camad', 'clinical', 'trial', 'terminated', 'difficulties', 'recruiting', 'patients'], ['data', 'safety', 'monitoring', 'board', 'agreement', 'findings', 'far', 'stopping', 'rule', 'met', 'suspends', 'treatment', 'arms', 'march'], ['dompé', 'decided', 'withdraw', 'numerous', 'difficulties', 'encountered', 'enrollment', 'mainly', 'rapidly', 'continuously', 'changing', 'oncology', 'drug', 'scenario', 'patients', 'enrolled'], ['funding', 'withdrawn', 'funding', 'source'], ['never', 'activated', 'enrollment'], ['business', 'regarding', 'drug'], ['lack', 'funding'], ['slow', 'patient', 'accrual'], ['financial', 'support'], ['determined', 'sufficient', 'enrollment', 'data', 'explore', 'impact', 'endopredict', 'test', 'endocrine', 'therapy', 'decisionmaking'], ['slow', 'recruitment', 'rate'], ['accrual', 'rate', 'date', 'low', 'finish', 'trial', 'reasonable', 'timeframe'], ['terminated', 'sponsor', 'lack', 'interest'], ['slow', 'accrual'], ['participants', 'enrolled'], ['difficulty', 'recruiting', 'retaining', 'participants'], ['delayed', 'start', 'technical', 'problems', 'well', 'covid', 'pandemic', 'investigator'], ['needle', 'production', 'stopped'], ['slow', 'recruitment', 'subject', 'profile'], ['ip', 'breach'], ['unable', 'recruit', 'patients', 'competing', 'studies'], ['halted', 'prematurely', 'prior', 'enrollment', 'first', 'participant'], ['funding', 'withdrawn'], ['pi', 'leaving', 'duke', 'position', 'another', 'state'], ['interim', 'analysis', 'provided', 'protocol', 'results', 'unsatisfactory'], ['limited', 'operating', 'room', 'availability'], ['enough', 'eligble', 'patient', 'found', 'many', 'screen', 'failures'], ['funder', 'decided', 'continue'], ['slow', 'accrual', 'partially', 'pandemic'], ['terminated', 'trial', 'initiated', 'new', 'one', 'including', 'pertuzumab'], ['funding'], ['low', 'accrual'], ['lack', 'funding'], ['drug', 'provider', 'decided', 'move', 'forward'], ['cessation', 'main', 'trial', 'biomarker', 'imaging', 'side'], ['unable', 'enroll', 'covid'], ['funding', 'discontinued'], ['sponsor'], ['pi', 'cancel', 'research'], ['disapproved', 'relevant', 'danish', 'ethical', 'committee'], ['recruitment', 'challenges', 'safety', 'concerns'], ['logistical', 'problems', 'meant', 'longer', 'feasible'], ['sponsor', 'withdrew', 'support'], ['china', 'experienced', 'new', 'waves', 'covid', 'since', 'january', 'chinese', 'government', 'adopted', 'strict', 'covid', 'policies', 'control', 'occasional', 'outbreaks', 'covid'], ['strategic', 'emerging', 'new', 'data', 'patients', 'hr', 'her', 'metastatic', 'breast', 'cancer'], ['insufficient', 'resources'], ['lack', 'funding'], ['business', 'perform'], ['design', 'modification'], ['business', 'based', 'inability', 'enroll', 'subjects', 'trial'], ['new', 'insights'], ['never', 'initiated', 'covid', 'pandemic', 'staffing', 'changes'], ['pharmaceutical', 'company', 'decided', 'withdraw', 'funding', 'provide', 'drug'], ['meeting', 'recruitment', 'goals'], ['pi', 'decided', 'withdraw'], ['prematurely', 'terminated', 'lack', 'recruitment', 'several', 'screening', 'failures', 'since', 'may', 'potential', 'candidate', 'cohort', 'found', 'gbrca', 'wild', 'type', 'hrd', 'score', 'mychoice', 'cdx', 'plus', 'test'], ['discontinue', 'based', 'broader', 'development', 'strategic', 'prioritisation', 'sponsor', 'concludes', 'benefitrisk', 'impact', 'co'], ['prematurely', 'discontinued', 'sponsor', 'probability', 'success', 'low', 'justify', 'continuation', 'recruitment'], ['company'], ['lost', 'funding'], ['sponsor', 'strategy', 'adjustment'], ['lack', 'effectiveness'], ['fact', 'sponsor', 'decided', 'move', 'forward', 'development'], ['per', 'sponsor'], ['low', 'accrual'], ['closure', 'radiolaboratory'], ['modification', 'care', 'habits', 'believe', 'today', 'longer', 'able', 'carry', 'initially', 'described'], ['insufficient', 'patient', 'inclusion'], ['funding'], ['ctp', 'lares', 'expire', 'date', 'trying', 'rws', 'pathway', 'indication', 'expend'], ['participants', 'enrolled'], ['meeting', 'fda', 'sparc', 'concludes', 'required'], ['early', 'terminated', 'low', 'enrollment', 'compared', 'anticipated', 'figures'], ['principal', 'investigator', 'wishes', 'revisit', 'design', 'start', 'new'], ['trial', 'initiated'], ['low', 'accrualloss', 'funding'], ['drug', 'manufacturer', 'terminate', 'development'], ['administrative', 'closure', 'based', 'sponsor', 'recommendation', 'prior', 'subject', 'enrollment'], ['principal', 'investigator', 'decided', 'move', 'forward'], ['sponsors', 'safety', 'concerns'], ['never', 'initiated', 'never', 'established', 'sites', 'enrolled', 'subjects'], ['inadequate', 'accrual', 'rate'], ['difficulty', 'recruiting', 'staffing', 'covid'], ['related', 'covid', 'continuing', 'data', 'analysis', 'time'], ['patients', 'enrolled', 'efforts', 'discontinued'], ['enrollment', 'incomplete', 'end', 'contracted', 'enrollment', 'period'], ['limited', 'recruitment'], ['participants', 'enrolled'], ['early', 'discontinuation', 'based', 'strategic', 'sponsor', 'driven', 'safety', 'concerns'], ['stopped', 'covid', 'pandemic', 'university', 'closed', 'since', 'beginning', 'pandemic', 'present', 'date', 'social', 'distance', 'imposed', 'government'], ['withdrawn'], ['application', 'withdrawn'], ['sponsor'], ['pi', 'left'], ['funding', 'terminated'], ['patients', 'screened', 'enrolled'], ['funding', 'issues', 'pharmacy', 'preparation', 'drug'], ['decided', 'halt', 'potentially', 'reopen', 'future'], ['unforeseen', 'slow', 'enrollment', 'shift', 'corporate', 'resources', 'covid', 'impact'], ['lack', 'resources'], ['sponsor', 'consequence', 'safety', 'concern'], ['accrual', 'suspended', 'pending', 'completion', 'amendment'], ['change', 'design'], ['dropped'], ['funding'], ['team', 'determined', 'data', 'collected', 'appropriate', 'outcome', 'goals'], ['low', 'enrollment'], ['logistic', 'reasons', 'operating', 'room', 'performing', 'surgery', 'investigated', 'moved', 'another', 'structure'], ['sponsor', 'discontinued', 'development', 'tesetaxel'], ['withdrawn', 'change', 'treatment', 'landscape', 'her', 'metastatic', 'breast', 'cancer'], ['per', 'sponsor', 'requestno', 'longer', 'manufacturing', 'drug'], ['funding', 'pi', 'leaving', 'institution'], ['sponsor', 'discontinued', 'development', 'tesetaxel'], ['hold', 'staffing', 'issues'], ['covid'], ['patients', 'recruited'], ['low', 'recruitment'], ['slow', 'recruitment', 'subject', 'profile'], ['trial', 'withdrawn', 'based', 'portfolio', 'prioritization', 'oral', 'atri', 'combination', 'niraparib', 'investigation', 'ddriver', 'solid', 'tumor'], ['manufacturer', 'supporting'], ['insufficient', 'recruitment'], ['terminated', 'early', 'given', 'first', 'four', 'patients', 'enrolled', 'experienced', 'grade', 'neutropenia', 'alopecia', 'cycle', 'failed', 'meet', 'primary', 'endpoint', 'main', 'secondary', 'endpoint'], ['principal', 'investigator', 'moving', 'new', 'institution', 'closing'], ['feasability', 'recruitment', 'issues'], ['lack', 'site', 'participation'], ['device', 'sent', 'back', 'repairs', 'returned', 'site', 'date'], ['trial', 'handovered', 'another', 'sponser'], ['feasibility', 'low', 'patient', 'accrual', 'financial', 'reasons'], ['halted', 'funding'], ['lack', 'enrollment'], ['lack', 'financial', 'human', 'resources', 'investigators', 'unable', 'continue', 'targeted', 'sample', 'size', 'achieved', 'recruitment', 'difficulties', 'mainly', 'related', 'covid', 'pandemic'], ['her', 'patient', 'develop', 'brain', 'mets', 'onafter', 'received', 'tdm', 'proven', 'insurmountable', 'challenge', 'recruit', 'patients'], ['pi', 'left', 'institution'], ['replaced', 'nct'], ['closed', 'prematurely', 'slow', 'accrual'], ['principal', 'investigator', 'retired', 'completed'], ['interim', 'analysis', 'algorithm', 'development'], ['recruitment', 'issues'], ['change', 'business', 'strategy'], ['agent', 'longer', 'available'], ['intervention', 'supply', 'interruption'], ['changes', 'standard', 'adjuvant', 'treatment', 'allow', 'iterative', 'picc', 'placement'], ['pi', 'withdrawn'], ['insufficient', 'staff'], ['terminated', 'review', 'asset', 'vbir', 'within', 'sponsors', 'oncology', 'portfolio', 'terminated', 'safety', 'concerns'], ['sponsor', 'safety', 'related'], ['closed', 'low', 'accrual'], ['reduction', 'available', 'resources'], ['low', 'accrual'], ['change', 'business', 'need'], ['pi'], ['terminated', 'slow', 'accrual'], ['funding', 'sponsor', 'filing', 'chapter', 'bankruptcy'], ['patient', 'enrolled', 'authorized', 'period'], ['sufficiently', 'staff', 'available', 'perform', 'trial'], ['diagnostic', 'issues'], ['revised', 'listed', 'us'], ['leading', 'entity', 'clinical', 'trial', 'replaced', 'patients', 'enrolled'], ['slow', 'inclusion', 'rate'], ['sponsor'], ['closing', 'clinical', 'partners', 'relocate', 'osu', 'could', 'find', 'continued', 'interest', 'however', 'demonstrated', 'feasibility', 'imaging', 'approach', 'also', 'filed', 'patent'], ['principal', 'investigator', 'departed', 'institution'], ['funding', 'unavailable', 'company', 'shutting'], ['enrollment', 'temporarily', 'halted', 'interim', 'analysis', 'ensure', 'adequate', 'evaluable', 'subjects'], ['funding'], ['suspended', 'qtultrasound', 'studies', 'reprioritized'], ['benefit', 'completing', 'worth', 'exposing', 'subjects', 'risk', 'covid', 'delaying', 'return', 'visits', 'would', 'make', 'difficult', 'analyze', 'changes', 'data', 'overtime'], ['slow', 'accrual', 'result', 'covid'], ['covid'], ['unable', 'meet', 'accrual', 'goal'], ['recruitment', 'temporarily', 'suspended', 'covid', 'resume', 'appropriate'], ['low', 'accrual', 'temporarily', 'suspended'], ['difficulty', 'recruiting', 'research', 'subjects'], ['quality', 'data', 'originating', 'prior', 'versions', 'protocol', 'affected', 'protocol', 'deviations', 'triggered', 'covid', 'pandemics'], ['abandoned', 'prior', 'opening', 'accrual', 'start'], ['change', 'development', 'priorities', 'clinical', 'development', 'lucitanib', 'plus', 'rucaparib', 'lucitanib', 'plus', 'sacituzumab', 'govitecan', 'combinations', 'planned', 'time'], ['sponsor'], ['stopped', 'early', 'increased', 'global', 'access', 'genomic', 'screening', 'longer', 'economical', 'continue', 'particular', 'singlegene', 'screening', 'protocol'], ['termination', 'business', 'safety', 'concerns'], ['progress', 'doesnt', 'meet', 'sponsors', 'requirement'], ['pi', 'left', 'institution', 'never', 'submitted', 'irb'], ['accrual'], ['strategic', 'business', 'unrelated', 'safety'], ['new', 'medical', 'team', 'surgical', 'center', 'location'], ['data', 'initial', 'patients', 'sufficient'], ['low', 'accrual'], ['stopped', 'unacceptable', 'toxicity', 'doseescalation', 'portion', 'phase', 'progress', 'phase'], ['clinical', 'treatment', 'patient', 'score', 'less', 'fact', 'ntx', 'score', 'points', 'weeks', 'application', 'albumin', 'bound', 'paclitaxel', 'project', 'therefore', 'suitable', 'subject', 'screening'], ['pi', 'left', 'institution'], ['halted', 'prematurely', 'slow', 'enrollment'], ['product', 'development', 'discontinued', 'unrelated', 'safety'], ['secondary', 'medicare', 'coverage', 'determination'], ['closed', 'portfolio', 'prioritization'], ['lack', 'funding'], ['unforeseen', 'complications', 'covid', 'funding'], ['award', 'yet', 'received', 'pi', 'transferring', 'different', 'institution'], ['withdrawn', 'scientific', 'interest', 'pursuing', 'syd', 'paclitaxel', 'combination', 'diminished'], ['high', 'number', 'screen', 'failures'], ['company', 'liquidated'], ['enrollment', 'challenges'], ['funding', 'withdrawn', 'sponsor'], ['unable', 'recruit', 'limited', 'number', 'patients', 'adh'], ['suspended', 'covid'], ['futility', 'recruitment'], ['resume', 'based', 'results', 'planned', 'interim', 'analysis', 'showed', 'futility'], ['sponsor', 'based', 'strategic', 'realignment'], ['mycotoxin', 'potential', 'contamination', 'one', 'lot', 'drug'], ['mainly', 'insufficient', 'recruitment'], ['slow', 'enrollment'], ['constraints', 'covid', 'unable', 'recruit', 'onsite'], ['never', 'started', 'change', 'standard', 'care', 'guideline'], ['clinical', 'hold', 'fda'], ['research', 'cancelled', 'inadequate', 'staffing'], ['similar', 'clinical', 'trials', 'showed', 'encouraging', 'results', 'made', 'us', 'decide', 'several', 'modifications', 'protocol', 'numerous', 'difficulties', 'encountered', 'abandon', 'trial'], ['sponsor', 'based', 'strategic', 'realignment'], ['funding', 'sought'], ['suspended', 'covid', 'pandemic', 'terminated', 'prevent', 'inconsistencies', 'baseline', 'anxiety', 'patients', 'enrolled', 'vs', 'covid', 'pandemic'], ['funding', 'available'], ['patient', 'feedback', 'principal', 'investigator', 'disponibility'], ['trial', 'initiated'], ['despite', 'demonstrated', 'safety', 'tolerability', 'trial', 'terminated', 'early', 'program', 'light', 'competitive', 'landscape'], ['departure', 'department', 'principal', 'investigator'], ['lack', 'human', 'ressources'], ['pi', 'left', 'nih'], ['pi', 'slow', 'accrual'], ['sponsor', 'terminated', 'dosing', 'dose', 'groups', 'pts', 'closed', 'trial', 'rtx', 'welltolerated', 'dlts', 'related', 'deaths', 'saes', 'gr', 'aes', 'cleared', 'rapidly', 'win', 'min'], ['enough', 'patients', 'initialize', 'clinical', 'trial'], ['design', 'changed', 'covid'], ['sponsor'], ['review', 'data', 'showed', 'low', 'likelihood', 'efficacy', 'patients', 'novartis', 'decided', 'terminate', 'trial', 'early', 'termination', 'safety', 'related'], ['critical', 'personnel', 'left', 'institution'], ['suspended', 'covid', 'pandemic'], ['terminated', 'mtd', 'reached'], ['stop', 'enrollment', 'strategic', 'considerations', 'specific', 'safety', 'reasons', 'request', 'regulatory', 'authority'], ['disapproved', 'moving', 'forward'], ['recruitment', 'stopped', 'target', 'sample', 'size', 'achieved'], ['business', 'strategy', 'change'], ['terminated', 'strategic', 'business', 'eli', 'lilly', 'company'], ['investigator', 'left', 'nih'], ['funded'], ['based', 'overall', 'results', 'phase', 'part', 'sponsor', 'decided', 'end', 'safety', 'reasons'], ['cami', 'combination', 'pembrolizumab', 'solid', 'tumors', 'showed', 'signals', 'activity', 'however', 'signals', 'insufficiently', 'compelling', 'tested', 'doseschedule', 'justify', 'continuation'], ['slow', 'accrual'], ['terminated', 'lack', 'enrollment', 'compounded', 'global', 'covid', 'pandemic', 'safety', 'andor', 'efficacy', 'concerns', 'involved', 'stop', 'enrollment'], ['participants', 'enrolled'], ['clear', 'benefit', 'gb', 'observed', 'either', 'monotherapy', 'combination', 'pembrolizumab'], ['zero', 'accrual'], ['business'], ['please', 'refer', 'nct'], ['prospective', 'recruitment', 'possible'], ['manufacturer', 'clovis', 'supplying', 'rucaparib', 'gone', 'bankrupt', 'longer', 'able', 'fund', 'trial', 'supply', 'product'], ['sponsor', 'based', 'portfolio', 'prioritization'], ['insufficient', 'fundingstaff'], ['subjects', 'eligible', 'closed'], ['unable', 'enroll', 'subjects'], ['data', 'longer', 'needed'], ['terminated', 'change', 'development', 'priorities'], ['business', 'reasons'], ['development', 'bdtx', 'discontinued', 'sponsor'], ['poor', 'enrollment'], ['closed', 'enrollment', 'data', 'analysis', 'recruitment', 'conducted', 'kaiser', 'permanente', 'msk', 'patients', 'recruited'], ['part', 'reached', 'original', 'enrollment', 'goal', 'protocol', 'amended', 'begin', 'enrollment', 'part', 'soon'], ['recommended', 'closure'], ['business', 'priorities'], ['technical', 'problem', 'blood', 'plasma', 'samples', 'obtained', 'hospital'], ['evolving', 'data', 'ipatasertib', 'changes', 'known', 'risk', 'benefit', 'background', 'pursuing', 'future', 'studies'], ['part', 'phd', 'trajectory', 'currently', 'achievability', 'question'], ['funding'], ['sponsor', 'campus', 'training', 'restrictions']]\n"
]
}
],
"source": [
"# TOKENIZE FILTERED RESPONSES\n",
"def sent_to_words(sentences):\n",
" for sentence in sentences:\n",
" yield(gensim.utils.simple_preprocess(str(sentence)))\n",
"\n",
"data = df_text.filtered_responses.values.tolist()\n",
"data_words = list(sent_to_words(data))\n",
"\n",
"print(data_words)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "ceffb2c6-18d2-4100-9518-59f86c8c9f06",
"metadata": {},
"outputs": [],
"source": [
"# CREATING TERM DICTINOARY OF CORPUS\n",
"dictionary = corpora.Dictionary(data_words)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "ec996c0f-2d87-4c0f-85a1-e93351dac814",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[(0, 1), (1, 1)], [(2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1)], [(9, 1), (10, 1), (11, 1)], [(12, 1), (13, 1), (14, 1), (15, 1)], [(16, 1), (17, 1), (18, 1), (19, 1)], [(20, 1), (21, 1)], [(22, 1), (23, 1), (24, 1), (25, 1), (26, 1)], [(27, 1), (28, 1), (29, 1), (30, 1), (31, 1), (32, 1), (33, 1)], [(34, 1), (35, 1), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1)], [(42, 1), (43, 1), (44, 1), (45, 1), (46, 1), (47, 1), (48, 1), (49, 1), (50, 1)], [(18, 1), (51, 1), (52, 1)], [(17, 1), (53, 1), (54, 1), (55, 1), (56, 1), (57, 1)], [(19, 1), (27, 1), (52, 1), (55, 1), (58, 1), (59, 1), (60, 1), (61, 1), (62, 1), (63, 1)], [(4, 1), (40, 1), (64, 1), (65, 1), (66, 1), (67, 1)], [(68, 1), (69, 1), (70, 1), (71, 1)], [(12, 1), (24, 1), (72, 1), (73, 1)], [(74, 1), (75, 1), (76, 1), (77, 1), (78, 1)], [(79, 1), (80, 1), (81, 1), (82, 1), (83, 1)], [(17, 1), (18, 1), (84, 1), (85, 1)], [(0, 1), (18, 1), (19, 1), (63, 1), (86, 1)], [(4, 1), (20, 1), (87, 1)], [(88, 1), (89, 1), (90, 1)], [(55, 1), (91, 1), (92, 1), (93, 1), (94, 1), (95, 1), (96, 1), (97, 1), (98, 1), (99, 1)], [(4, 1), (40, 1), (93, 1), (99, 1), (100, 1), (101, 1), (102, 1), (103, 1), (104, 1), (105, 1), (106, 1), (107, 1), (108, 1), (109, 2)], [(110, 1)], [(17, 1), (27, 1), (55, 1), (111, 1), (112, 1), (113, 1), (114, 1), (115, 1), (116, 1), (117, 1), (118, 1)], [(18, 1), (119, 1)], [(58, 1), (120, 1)], [(0, 1), (79, 1), (121, 1)], [(12, 1), (24, 1), (61, 1), (73, 1)], [(122, 1), (123, 1), (124, 1)], [(91, 1), (109, 1), (125, 1), (126, 1), (127, 1), (128, 1), (129, 1), (130, 1), (131, 1), (132, 1)], [(0, 1), (79, 1), (133, 1), (134, 1), (135, 1), (136, 1), (137, 1), (138, 1), (139, 2), (140, 2)], [(55, 1), (92, 1), (141, 1), (142, 1), (143, 1)], [(144, 1), (145, 1), (146, 1)], [(94, 1), (147, 1), (148, 1), (149, 1), (150, 1)], [(90, 1), (120, 1), (151, 1), (152, 1)], [(27, 1), (153, 1), (154, 1), (155, 1), (156, 1)], [(13, 1), (15, 1), (157, 1)], [(79, 1), (81, 1), (158, 1)], [(159, 1), (160, 1), (161, 1)], [(20, 1), (162, 1), (163, 1), (164, 1)], [(46, 1)], [(46, 1), (52, 1), (56, 1), (83, 1), (99, 1), (154, 1), (165, 1), (166, 1), (167, 1), (168, 1), (169, 1), (170, 1), (171, 1), (172, 1), (173, 1)], [(109, 1), (174, 1), (175, 1)], [(150, 1), (176, 1)], [(55, 1), (99, 2), (159, 1), (177, 1), (178, 1), (179, 1), (180, 1), (181, 1), (182, 1), (183, 1), (184, 2), (185, 1), (186, 1)], [(61, 1), (187, 1), (188, 1), (189, 1)], [(7, 1), (85, 1), (190, 1), (191, 1), (192, 1), (193, 1)], [(58, 1), (164, 1), (194, 1), (195, 1), (196, 1)], [(197, 1)], [(4, 1), (83, 1), (93, 1), (99, 1), (119, 1), (198, 1), (199, 1)], [(27, 1), (48, 1), (55, 1), (109, 1), (200, 1), (201, 1), (202, 1), (203, 1), (204, 1), (205, 1), (206, 1), (207, 1), (208, 1), (209, 1)], [(4, 1), (20, 1), (46, 1), (74, 1), (119, 1), (210, 1), (211, 1), (212, 1), (213, 1), (214, 1), (215, 1), (216, 1), (217, 1), (218, 1), (219, 1), (220, 1)], [(143, 1), (151, 2), (221, 1)], [(46, 1), (81, 1), (222, 1)], [(149, 1), (178, 1), (213, 1)], [(120, 1), (151, 1)], [(52, 1), (58, 1), (223, 1)], [(224, 1), (225, 1)], [(27, 1), (46, 1), (226, 1), (227, 1), (228, 1), (229, 1), (230, 1), (231, 1), (232, 1), (233, 1), (234, 1)], [(18, 1), (51, 1), (52, 1)], [(51, 1), (58, 1), (83, 1), (85, 1), (235, 1), (236, 1), (237, 1), (238, 1)], [(56, 1), (99, 1), (120, 1), (124, 1)], [(52, 1), (58, 1)], [(20, 1), (21, 1)], [(21, 1), (196, 1), (199, 1), (239, 1)], [(0, 1), (8, 1), (11, 1), (13, 1), (79, 1), (240, 1), (241, 1), (242, 1)], [(19, 1), (243, 1), (244, 1)], [(18, 1), (52, 1), (245, 1), (246, 1)], [(247, 1), (248, 1)], [(4, 1), (66, 1), (70, 1), (249, 1), (250, 1)], [(17, 1), (46, 1), (63, 1), (84, 1), (251, 1), (252, 1)], [(143, 1), (151, 1)], [(24, 1), (153, 1), (253, 1), (254, 1), (255, 1), (256, 1)], [(28, 1), (257, 1), (258, 1), (259, 1), (260, 1), (261, 1)], [(145, 1), (262, 1), (263, 1), (264, 1)], [(223, 1), (265, 1), (266, 1), (267, 1), (268, 1), (269, 1), (270, 1)], [(74, 1), (197, 1), (271, 1)], [(0, 1), (52, 1), (58, 1), (272, 1)], [(61, 1), (62, 1), (83, 1), (99, 1), (181, 1), (273, 1), (274, 1)], [(151, 1)], [(58, 1), (85, 1)], [(120, 1), (151, 1)], [(74, 1), (213, 1), (275, 1), (276, 1), (277, 1)], [(83, 1), (278, 1), (279, 1), (280, 1), (281, 1), (282, 1)], [(70, 1), (79, 1), (103, 1)], [(113, 1), (151, 1)], [(56, 1)], [(24, 1), (283, 1), (284, 1)], [(35, 1), (285, 1), (286, 1), (287, 1), (288, 1)], [(18, 1), (55, 1), (112, 1), (289, 1)], [(23, 1), (127, 1), (241, 1), (290, 1), (291, 1)], [(56, 1), (225, 1), (292, 1)], [(61, 1), (79, 3), (293, 1), (294, 1), (295, 1), (296, 1), (297, 1), (298, 1), (299, 1), (300, 1), (301, 1), (302, 1), (303, 1), (304, 1), (305, 1)], [(4, 1), (27, 1), (61, 1), (100, 1), (101, 1), (150, 1), (306, 1), (307, 1), (308, 1), (309, 1)], [(16, 1), (310, 1)], [(120, 1), (151, 1)], [(178, 1), (311, 1)], [(312, 1), (313, 1)], [(83, 1), (91, 1), (103, 1), (164, 1), (178, 1), (314, 1)], [(61, 1), (315, 1)], [(0, 1), (79, 1), (81, 1), (147, 1), (181, 1), (316, 1)], [(60, 1), (74, 1), (151, 1), (213, 1), (220, 1), (317, 1), (318, 1)], [(18, 1), (47, 1), (319, 1)], [(24, 1), (74, 1), (220, 1)], [(17, 1), (18, 1), (99, 1), (120, 1), (233, 1), (267, 1), (268, 1), (303, 1), (320, 1), (321, 1), (322, 1), (323, 1), (324, 1), (325, 1), (326, 1), (327, 1), (328, 1), (329, 1), (330, 1), (331, 1), (332, 1), (333, 1)], [(56, 1), (91, 1), (94, 1), (150, 1), (231, 1), (334, 1), (335, 1), (336, 1), (337, 1), (338, 1), (339, 1)], [(17, 1), (18, 1), (56, 1), (85, 1), (113, 1), (340, 1), (341, 1), (342, 1), (343, 1)], [(60, 1)], [(151, 1), (344, 1)], [(56, 1), (98, 1), (345, 1)], [(120, 1), (346, 1)], [(56, 1), (74, 1), (94, 1), (275, 1), (276, 1), (347, 1)], [(30, 1), (56, 1)], [(58, 1), (85, 1)], [(348, 1), (349, 1)], [(23, 1), (65, 1), (144, 1), (313, 1), (350, 1), (351, 1), (352, 1), (353, 1), (354, 1), (355, 1)], [(16, 1), (223, 1), (356, 1)], [(151, 1)], [(235, 1), (357, 1), (358, 1), (359, 1), (360, 1), (361, 1), (362, 1), (363, 1), (364, 1)], [(20, 1), (21, 1)], [(319, 1), (337, 1), (365, 1), (366, 1), (367, 1)], [(46, 1), (85, 1), (99, 1), (368, 1), (369, 1), (370, 1), (371, 1)], [(11, 1), (13, 1), (15, 1), (61, 1), (312, 1), (372, 1), (373, 1)], [(83, 1), (181, 1)], [(85, 1), (151, 1), (374, 1)], [(94, 1), (118, 1), (213, 1), (375, 1)], [(46, 1), (56, 1), (91, 1), (246, 1), (252, 1), (348, 1), (376, 1), (377, 1)], [(13, 1), (15, 1), (74, 1), (275, 1), (276, 1)], [(55, 1), (112, 1), (378, 1)], [(20, 1), (81, 2), (164, 1), (181, 1), (379, 1), (380, 1)], [(51, 1), (58, 1), (193, 1)], [(79, 1), (196, 1), (199, 1), (316, 1)], [(27, 1), (79, 1), (97, 1), (257, 1), (381, 1), (382, 1)], [(4, 1), (20, 1), (113, 1), (383, 1)], [(46, 2), (384, 1), (385, 1), (386, 1), (387, 1)], [(18, 1), (145, 1)], [(20, 1), (21, 1)], [(55, 1), (56, 1), (91, 1), (112, 1), (150, 1), (370, 1), (388, 1), (389, 1)], [(0, 2), (19, 1), (25, 1), (79, 1), (195, 1), (235, 1), (298, 1), (303, 1), (390, 1), (391, 1), (392, 1), (393, 1), (394, 1)], [(143, 1)], [(143, 1), (395, 1)], [(56, 1)], [(14, 1), (24, 1)], [(99, 1), (151, 1)], [(4, 1), (20, 1), (396, 1)], [(114, 1), (151, 1), (213, 1), (397, 1), (398, 1)], [(74, 1), (75, 1), (76, 1), (77, 1), (78, 1)], [(46, 1), (52, 1), (79, 1), (231, 1), (310, 1), (399, 1), (400, 1), (401, 1)], [(120, 1), (310, 1)], [(53, 1), (55, 1), (56, 1), (402, 1)], [(58, 1), (403, 1), (404, 1), (405, 1), (406, 1)], [(92, 1), (312, 1)], [(407, 1)], [(151, 1)], [(27, 1), (32, 1), (47, 1), (227, 1), (408, 1), (409, 1), (410, 1)], [(46, 1), (85, 1)], [(138, 1), (142, 1), (153, 1), (263, 1), (264, 1), (411, 1), (412, 1), (413, 1), (414, 1), (415, 1)], [(56, 1), (94, 1), (113, 1), (416, 1)], [(92, 1), (100, 1), (101, 1), (109, 1), (143, 1), (307, 1), (309, 1), (417, 1)], [(23, 1), (30, 1), (56, 1), (213, 1), (418, 1), (419, 1)], [(12, 1), (24, 1), (151, 1), (254, 1)], [(56, 1), (94, 1), (113, 1), (416, 1)], [(114, 1), (316, 1), (420, 1)], [(79, 1)], [(4, 1), (421, 1)], [(18, 1), (85, 1)], [(18, 1), (52, 1), (245, 1), (246, 1)], [(83, 1), (91, 1), (143, 1), (422, 1), (423, 1), (424, 1), (425, 1), (426, 1), (427, 1), (428, 1), (429, 1), (430, 1), (431, 1)], [(375, 1), (432, 1)], [(16, 1), (18, 1)], [(4, 1), (20, 1), (50, 1), (99, 1), (251, 1), (281, 1), (297, 1), (370, 1), (433, 1), (434, 1), (435, 2), (436, 1), (437, 1), (438, 1), (439, 1), (440, 1), (441, 1), (442, 1)], [(12, 1), (13, 1), (15, 1), (61, 1), (73, 1), (443, 1)], [(18, 1), (114, 1), (444, 1)], [(120, 1), (445, 1), (446, 1)], [(235, 1), (446, 1), (447, 1), (448, 1), (449, 1), (450, 1), (451, 1)], [(83, 1), (153, 1), (452, 1), (453, 1)], [(58, 1), (85, 1), (142, 1), (223, 1), (224, 1), (454, 1)], [(84, 1), (151, 1)], [(46, 1), (120, 1)], [(0, 1), (18, 1), (37, 1), (70, 1), (79, 1), (97, 1), (119, 1), (120, 1), (215, 1), (224, 1), (271, 1), (310, 1), (455, 1), (456, 1), (457, 1), (458, 1), (459, 1)], [(4, 1), (66, 1), (106, 1), (223, 1), (307, 1), (460, 1), (461, 1), (462, 1), (463, 1), (464, 1), (465, 1), (466, 1), (467, 1)], [(12, 1), (14, 1), (24, 1)], [(468, 1), (469, 1)], [(17, 1), (52, 1), (58, 1), (195, 1)], [(13, 1), (15, 1), (470, 1), (471, 1)], [(28, 1), (94, 1), (257, 1), (472, 1)], [(18, 1), (114, 1)], [(92, 1), (98, 1), (178, 1)], [(23, 1), (473, 1), (474, 1)], [(475, 1), (476, 1), (477, 1)], [(109, 1), (147, 1), (175, 1), (478, 1), (479, 1), (480, 1), (481, 1), (482, 1)], [(24, 1), (143, 1)], [(16, 1), (146, 1)], [(31, 1), (55, 1), (99, 2), (112, 1), (177, 1), (217, 1), (378, 1), (428, 1), (483, 1), (484, 1)], [(55, 1), (56, 1), (97, 1)], [(58, 1), (85, 1), (195, 1)], [(310, 1), (474, 1), (485, 1)], [(58, 1), (85, 1)], [(92, 1), (178, 1), (486, 1)], [(24, 1)], [(52, 1), (58, 1), (99, 1)], [(56, 1), (151, 1), (487, 1), (488, 1), (489, 1)], [(20, 1), (223, 1), (387, 1), (490, 1)], [(83, 1), (146, 1), (311, 1), (474, 1), (491, 1)], [(114, 1), (492, 1)], [(188, 1), (493, 1), (494, 1)], [(4, 1), (20, 1), (83, 1), (93, 1), (469, 1), (495, 1), (496, 1)], [(51, 1), (52, 1), (356, 1)], [(56, 1)], [(93, 1), (124, 1), (129, 1), (280, 1), (443, 1), (454, 1), (497, 1), (498, 1), (499, 1), (500, 1), (501, 1), (502, 1), (503, 1), (504, 1), (505, 1), (506, 1), (507, 1)], [(12, 1), (13, 1), (15, 1), (508, 1)], [(60, 1), (151, 1), (509, 1), (510, 1)], [(28, 1), (33, 1), (46, 1), (84, 1), (164, 1), (257, 1), (511, 1), (512, 1), (513, 1)], [(151, 1)], [(250, 1), (406, 1), (514, 1), (515, 1)], [(27, 1), (79, 1), (102, 1), (147, 1), (164, 1), (516, 1), (517, 1), (518, 1), (519, 1), (520, 1), (521, 1), (522, 1), (523, 1), (524, 1), (525, 1), (526, 1), (527, 1)], [(52, 1), (58, 1), (79, 1), (528, 1)], [(79, 1)], [(58, 1), (70, 1), (440, 1), (529, 1)], [(18, 1), (33, 1), (79, 1), (406, 1), (408, 1), (530, 1)], [(33, 1), (58, 1), (85, 1), (406, 1)], [(164, 1), (196, 1), (199, 1), (284, 1)], [(27, 1), (79, 1), (116, 1), (252, 1), (258, 2), (531, 1), (532, 1), (533, 1), (534, 1), (535, 1), (536, 1)], [(11, 1), (58, 1), (64, 1), (252, 1), (537, 1)], [(92, 1), (93, 1), (94, 2), (327, 2), (382, 1), (538, 1), (539, 1), (540, 2), (541, 1), (542, 1), (543, 1), (544, 1)], [(56, 1)], [(19, 1), (23, 1), (258, 1), (271, 1), (330, 2), (370, 1), (545, 1), (546, 1), (547, 1), (548, 1), (549, 1), (550, 1), (551, 1)], [(55, 1), (90, 1), (112, 1), (178, 1)], [(378, 1), (440, 1), (552, 1), (553, 1), (554, 1)], [(12, 1), (14, 1), (24, 1), (81, 1), (555, 1), (556, 1)], [(58, 1)], [(55, 1), (150, 1), (178, 1), (557, 1)], [(32, 1), (61, 1), (108, 1), (558, 1), (559, 1), (560, 1)], [(4, 1), (27, 1), (232, 1), (561, 1)], [(58, 1), (85, 1)], [(19, 1), (161, 1), (553, 1), (562, 1), (563, 2), (564, 1), (565, 1)], [(40, 1), (93, 1), (109, 1), (223, 1), (246, 1), (329, 2), (330, 1), (347, 1), (395, 1), (566, 1), (567, 1), (568, 1), (569, 1), (570, 1), (571, 1), (572, 1), (573, 1), (574, 1)], [(12, 1), (14, 1), (24, 1)], [(17, 1), (46, 1), (52, 1), (84, 1)], [(55, 1), (94, 1), (113, 1), (148, 1), (557, 1)], [(442, 1), (575, 1), (576, 1), (577, 1)], [(195, 1), (428, 1), (429, 1)], [(120, 1), (151, 1)], [(79, 1), (151, 1), (401, 1), (578, 1)], [(12, 1), (24, 1), (72, 1), (106, 1), (579, 1), (580, 1), (581, 1)], [(121, 1), (124, 1), (143, 1), (423, 1), (570, 1), (582, 1), (583, 1), (584, 1)], [(267, 1), (270, 1), (585, 1), (586, 1)], [(60, 1), (587, 1)], [(46, 1), (289, 1)], [(56, 1), (143, 1), (151, 1)], [(4, 1), (66, 1), (70, 1), (145, 1), (586, 1), (588, 1)], [(79, 1), (406, 1)], [(18, 1), (589, 1)], [(28, 1), (91, 1), (257, 1), (260, 1), (530, 1), (541, 1), (589, 1), (590, 1)], [(56, 1), (91, 1), (150, 1), (591, 1)], [(62, 1), (213, 1), (328, 1), (592, 1), (593, 1), (594, 1)], [(16, 1), (18, 1), (215, 1)], [(46, 1), (52, 1)], [(66, 1), (70, 1), (79, 1), (595, 1), (596, 1)], [(81, 1), (92, 1), (158, 1), (175, 1), (351, 1), (597, 1)], [(93, 1), (365, 1), (420, 1)], [(193, 1), (284, 1), (316, 1), (598, 1)], [(83, 1), (93, 1), (105, 1), (119, 1), (214, 1), (216, 1), (258, 1), (260, 1), (331, 1), (494, 1), (590, 1), (599, 1), (600, 1), (601, 1), (602, 1), (603, 1), (604, 1)], [(56, 1), (91, 1), (150, 1), (591, 1)], [(151, 1), (605, 1)], [(0, 2), (4, 1), (20, 1), (79, 2), (99, 1), (406, 1), (606, 1), (607, 1), (608, 1), (609, 1), (610, 1)], [(151, 1), (474, 1)], [(13, 1), (15, 1), (223, 1), (611, 1), (612, 1)], [(83, 1), (181, 1)], [(55, 1), (83, 1), (99, 1), (370, 1), (417, 1), (501, 1), (613, 1), (614, 1), (615, 1), (616, 1), (617, 1)], [(13, 1), (15, 1), (618, 1), (619, 1)], [(37, 1), (120, 1), (620, 1)], [(14, 1), (24, 1), (621, 1)], [(24, 1), (52, 1), (58, 1)], [(56, 1), (83, 1), (97, 1), (99, 1), (159, 1), (195, 1), (218, 1), (622, 1), (623, 1), (624, 1), (625, 1), (626, 1), (627, 1), (628, 1), (629, 1), (630, 1), (631, 1), (632, 1), (633, 1), (634, 1)], [(4, 1), (83, 1), (93, 1), (266, 1), (635, 1)], [(79, 1), (174, 1), (312, 1)], [(56, 1)], [(4, 1), (27, 1), (31, 1), (55, 1), (74, 1), (83, 1), (85, 1), (90, 1), (95, 1), (97, 1), (118, 1), (370, 1), (590, 1), (636, 1), (637, 1)], [(12, 1), (14, 1), (638, 1), (639, 1)], [(0, 1), (79, 1), (406, 1)], [(99, 1), (640, 1), (641, 1)], [(46, 1), (55, 1), (57, 1), (142, 1), (150, 1), (176, 1), (642, 1), (643, 1), (644, 1), (645, 1)], [(73, 1), (275, 1), (286, 1)], [(18, 1), (19, 1), (172, 1), (455, 1), (457, 1), (458, 1)], [(92, 1), (98, 1), (178, 1)], [(60, 1), (99, 1), (150, 1), (178, 1), (646, 1), (647, 1)], [(13, 1), (14, 1), (621, 1)], [(648, 1)], [(55, 1), (56, 1), (74, 1), (91, 1), (142, 1), (184, 1), (260, 1), (385, 1), (563, 1), (649, 1)], [(129, 1), (340, 1), (341, 1), (423, 1), (430, 1), (590, 1), (650, 1), (651, 1), (652, 1), (653, 1), (654, 1), (655, 1), (656, 2), (657, 1), (658, 1)], [(52, 1), (58, 1)], [(0, 1), (46, 2), (55, 1), (57, 1), (79, 1), (95, 1), (99, 1), (112, 1), (120, 1), (548, 1), (659, 1), (660, 1), (661, 1)], [(20, 1), (21, 1)], [(423, 1), (517, 1), (655, 1), (662, 1), (663, 1), (664, 1), (665, 1), (666, 1)], [(58, 1), (667, 1)], [(178, 1)], [(468, 1), (668, 1), (669, 1)], [(18, 1), (670, 1), (671, 1)], [(23, 1), (65, 1), (83, 1), (148, 1), (375, 1), (477, 1), (543, 1), (672, 1), (673, 1), (674, 1), (675, 1), (676, 1)], [(56, 1), (91, 1), (428, 1), (429, 1)], [(16, 1), (677, 1)], [(164, 1), (195, 1), (678, 1)], [(70, 1), (103, 1), (164, 1)], [(23, 1), (27, 1), (679, 1)], [(92, 1), (94, 1), (99, 1), (542, 1)], [(142, 1), (178, 1)], [(56, 1), (94, 1), (113, 1), (680, 1)], [(46, 1), (87, 1)], [(4, 1), (18, 1), (27, 1), (46, 1), (195, 1), (257, 1), (421, 1), (681, 1), (682, 1), (683, 1), (684, 1)], [(46, 2), (184, 2), (258, 1), (529, 1), (641, 1), (685, 1), (686, 1), (687, 1), (688, 1)], [(348, 1), (689, 1)], [(178, 1), (542, 1)], [(2, 1), (3, 1), (5, 1), (6, 1), (7, 1), (8, 1), (690, 1)], [(27, 1), (75, 1), (121, 1), (147, 1), (250, 1), (517, 1), (524, 1), (691, 1), (692, 1), (693, 1), (694, 1)], [(9, 1), (184, 1), (695, 1), (696, 1), (697, 1), (698, 1)], [(151, 1)], [(56, 1), (699, 1), (700, 1), (701, 1)]]\n"
]
}
],
"source": [
"# CONVERT CORPUS INTO DOC-TERM MATRIX\n",
"doc_term_matrix = [dictionary.doc2bow(doc) for doc in data_words]\n",
"\n",
"print(doc_term_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "67479119-b9d3-4a2e-afe4-8a8d4b362305",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[('pandemic', 1), ('situation', 1)],\n",
" [('blood', 1),\n",
" ('obtained', 1),\n",
" ('patients', 1),\n",
" ('plasma', 1),\n",
" ('problem', 1),\n",
" ('samples', 1),\n",
" ('technical', 1)],\n",
" [('currently', 1), ('pause', 1), ('start', 1)],\n",
" [('institution', 1), ('investigator', 1), ('left', 1), ('principal', 1)],\n",
" [('insufficient', 1), ('prematurely', 1), ('recruitment', 1), ('stopped', 1)],\n",
" [('enrolled', 1), ('participants', 1)],\n",
" [('indiana', 1), ('longer', 1), ('pi', 1), ('university', 1), ('working', 1)],\n",
" [('data', 1),\n",
" ('interim', 1),\n",
" ('paused', 1),\n",
" ('per', 1),\n",
" ('review', 1),\n",
" ('team', 1),\n",
" ('temporarily', 1)],\n",
" [('classified', 1),\n",
" ('committee', 1),\n",
" ('ethics', 1),\n",
" ('human', 1),\n",
" ('involving', 1),\n",
" ('person', 1),\n",
" ('project', 1),\n",
" ('scope', 1)],\n",
" [('aware', 1),\n",
" ('cohorts', 1),\n",
" ('concluded', 1),\n",
" ('core', 1),\n",
" ('enrollment', 1),\n",
" ('goals', 1),\n",
" ('met', 1),\n",
" ('objective', 1),\n",
" ('primary', 1)]]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[[(dictionary[i], freq) for i, freq in doc] for doc in doc_term_matrix[:10]]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "2053e842-a272-49bd-965d-09038a6c2fc3",
"metadata": {},
"outputs": [],
"source": [
"# CREATING THE OBJECT FOR LDA MODEL \n",
"Lda = gensim.models.ldamodel.LdaModel"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "45bad93d-c0ec-4e46-81b0-4eec58112590",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, '0.032*\"enrollment\" + 0.022*\"terminated\" + 0.021*\"pi\" + 0.019*\"accrual\" + 0.015*\"institution\" + 0.013*\"enrolled\" + 0.012*\"low\" + 0.011*\"patients\" + 0.011*\"left\" + 0.008*\"slow\"'), (1, '0.036*\"sponsor\" + 0.022*\"safety\" + 0.018*\"development\" + 0.016*\"business\" + 0.016*\"slow\" + 0.014*\"accrual\" + 0.014*\"data\" + 0.014*\"strategic\" + 0.014*\"change\" + 0.013*\"based\"'), (2, '0.035*\"funding\" + 0.028*\"recruitment\" + 0.028*\"covid\" + 0.012*\"lack\" + 0.011*\"pandemic\" + 0.009*\"patients\" + 0.009*\"insufficient\" + 0.009*\"subjects\" + 0.009*\"trial\" + 0.008*\"never\"')]\n"
]
}
],
"source": [
"# RUNNING AND TRAINING LDA MODEL\n",
"ldamodel = Lda(\n",
" doc_term_matrix, num_topics=3, id2word=dictionary, passes=50)\n",
"\n",
"print(ldamodel.print_topics())"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "c02bb604-dfa1-4de5-bc96-d0040cce74ad",
"metadata": {},
"outputs": [],
"source": [
"import pyLDAvis\n",
"import pickle\n",
"import pyLDAvis.gensim_models"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "9ab87623-c839-4682-a9c3-74a837079069",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
"\n",
"\n",
"<div id=\"ldavis_el302711402825237329446870855297\"></div>\n",
"<script type=\"text/javascript\">\n",
"\n",
"var ldavis_el302711402825237329446870855297_data = {\"mdsDat\": {\"x\": [0.12495862603628331, -0.009108257919659966, -0.11585036811662332], \"y\": [0.05200471301188651, -0.11732204477215984, 0.06531733176027336], \"topics\": [1, 2, 3], \"cluster\": [1, 1, 1], \"Freq\": [34.07361161861341, 33.859390967883556, 32.06699741350303]}, \"tinfo\": {\"Term\": [\"funding\", \"enrollment\", \"sponsor\", \"recruitment\", \"pi\", \"development\", \"safety\", \"institution\", \"business\", \"strategic\", \"accrual\", \"change\", \"covid\", \"slow\", \"terminated\", \"left\", \"data\", \"based\", \"insufficient\", \"enrolled\", \"lack\", \"participants\", \"issues\", \"pandemic\", \"rate\", \"investigator\", \"halted\", \"recruit\", \"resources\", \"principal\", \"funding\", \"insufficient\", \"issues\", \"recruit\", \"recruitment\", \"resources\", \"limited\", \"available\", \"difficulty\", \"enroll\", \"continue\", \"staff\", \"standard\", \"manufacturer\", \"endpoint\", \"experienced\", \"signals\", \"government\", \"pembrolizumab\", \"achieved\", \"sample\", \"size\", \"continuation\", \"justify\", \"screen\", \"operating\", \"room\", \"supply\", \"number\", \"reopen\", \"covid\", \"subjects\", \"unable\", \"never\", \"longer\", \"lack\", \"pandemic\", \"stopped\", \"treatment\", \"initiated\", \"design\", \"recruiting\", \"staffing\", \"patients\", \"trial\", \"another\", \"closed\", \"enrolled\", \"decided\", \"withdrawn\", \"patient\", \"low\", \"discontinued\", \"sponsor\", \"pi\", \"institution\", \"enrollment\", \"left\", \"participants\", \"halted\", \"interest\", \"moving\", \"prior\", \"portfolio\", \"score\", \"prioritization\", \"plus\", \"test\", \"demonstrated\", \"times\", \"wait\", \"paclitaxel\", \"oncology\", \"imaging\", \"asset\", \"closing\", \"different\", \"sufficient\", \"reached\", \"application\", \"nih\", \"poor\", \"recruited\", \"disapproved\", \"part\", \"terminated\", \"enrolled\", \"accrual\", \"low\", \"protocol\", \"patients\", 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0.4125972022908637, 0.6188958034362956, 0.9592510220992211, 0.9597491831645537, 0.2880226054898263, 0.5760452109796526, 0.9626518277451012, 0.959250652369819, 0.2619823733419429, 0.720451526690343, 0.9592494768301155, 0.7117188985032726, 0.9592506104692207, 0.2888199349137084, 0.5776398698274168, 0.9592484722944826, 0.9592494768301155, 0.3027726062776772, 0.6055452125553544, 0.14000902743363677, 0.8867238404130329, 0.7196219333738589, 0.5738852781170886, 0.2869426390585443, 0.7196418716632738, 0.6410643926263041, 0.32053219631315205, 0.9207585334813339, 0.8673585018290949, 0.7193334868889149, 0.14386669737778296, 0.14386669737778296, 0.9498853073761546, 0.9592508061403632, 0.722494615507807, 0.7224625368636819, 0.7224581823650912, 0.12399358784022514, 0.7439615270413508, 0.12399358784022514, 0.8673191511740231, 0.962652760921068, 0.9498794928664006, 0.9498802863877911, 0.6367922075800274, 0.3183961037900137, 0.35933928312979574, 0.28747142650383656, 0.35933928312979574, 0.6406564979778322, 0.3203282489889161, 0.9626524210468583, 0.962652009585934, 0.9498802863877911, 0.39027331906214274, 0.5203644254161903, 0.13009110635404758], \"Term\": [\"accrual\", \"accrual\", \"achieved\", \"another\", \"another\", \"application\", \"asset\", \"available\", \"based\", \"based\", \"blood\", \"business\", \"business\", \"change\", \"changes\", \"changes\", \"clinical\", \"clinical\", \"clinical\", \"closed\", \"closed\", \"closing\", \"closure\", \"concern\", \"concerns\", \"concerns\", \"concludes\", \"continuation\", \"continue\", \"covid\", \"covid\", \"covid\", \"data\", \"data\", \"decided\", \"decided\", \"decided\", \"demonstrated\", \"design\", \"design\", \"development\", \"different\", \"difficulty\", \"disapproved\", \"discontinued\", \"discontinued\", \"drug\", \"drug\", \"drug\", \"endpoint\", \"enroll\", \"enrolled\", \"enrolled\", \"enrollment\", \"enrollment\", \"experienced\", \"forward\", \"funding\", \"government\", \"halted\", \"imaging\", \"initiated\", \"initiated\", \"institution\", \"insufficient\", \"interest\", \"investigator\", \"investigator\", \"issues\", \"justify\", \"lack\", \"lack\", \"left\", \"limited\", \"longer\", \"longer\", \"low\", \"low\", \"low\", \"manufacturer\", \"move\", \"moving\", \"never\", \"never\", \"new\", \"new\", \"new\", \"nih\", \"number\", \"obtained\", \"oncology\", \"operating\", \"paclitaxel\", \"pandemic\", \"pandemic\", \"part\", \"part\", \"participants\", \"patient\", \"patient\", \"patient\", \"patients\", \"patients\", \"patients\", \"pembrolizumab\", \"per\", \"phase\", \"pi\", \"plasma\", \"plus\", \"poor\", \"portfolio\", \"prematurely\", \"prematurely\", \"prematurely\", \"principal\", \"principal\", \"prior\", \"priorities\", \"prioritization\", \"problem\", \"progress\", \"protocol\", \"protocol\", \"protocol\", \"rate\", \"reached\", \"realignment\", \"reasons\", \"reasons\", \"recruit\", \"recruited\", \"recruiting\", \"recruiting\", \"recruitment\", \"recruitment\", \"regarding\", \"related\", \"related\", \"reopen\", \"resources\", \"results\", \"results\", \"risk\", \"room\", \"safety\", \"safety\", \"sample\", \"score\", \"screen\", \"showed\", \"showed\", \"signals\", \"size\", \"slow\", \"slow\", \"sponsor\", \"sponsor\", \"staff\", \"staffing\", \"staffing\", \"standard\", \"stopped\", \"stopped\", \"strategic\", \"strategy\", \"subjects\", \"subjects\", \"subjects\", \"sufficient\", \"supply\", \"team\", \"technical\", \"terminate\", \"terminated\", \"terminated\", \"terminated\", \"termination\", \"tesetaxel\", \"test\", \"times\", \"treatment\", \"treatment\", \"trial\", \"trial\", \"trial\", \"unable\", \"unable\", \"unrelated\", \"us\", \"wait\", \"withdrawn\", \"withdrawn\", \"withdrawn\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 2]};\n",
"\n",
"function LDAvis_load_lib(url, callback){\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = true;\n",
" s.onreadystatechange = s.onload = callback;\n",
" s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
"}\n",
"\n",
"if(typeof(LDAvis) !== \"undefined\"){\n",
" // already loaded: just create the visualization\n",
" !function(LDAvis){\n",
" new LDAvis(\"#\" + \"ldavis_el302711402825237329446870855297\", ldavis_el302711402825237329446870855297_data);\n",
" }(LDAvis);\n",
"}else if(typeof define === \"function\" && define.amd){\n",
" // require.js is available: use it to load d3/LDAvis\n",
" require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
" require([\"d3\"], function(d3){\n",
" window.d3 = d3;\n",
" LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
" new LDAvis(\"#\" + \"ldavis_el302711402825237329446870855297\", ldavis_el302711402825237329446870855297_data);\n",
" });\n",
" });\n",
"}else{\n",
" // require.js not available: dynamically load d3 & LDAvis\n",
" LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
" LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
" new LDAvis(\"#\" + \"ldavis_el302711402825237329446870855297\", ldavis_el302711402825237329446870855297_data);\n",
" })\n",
" });\n",
"}\n",
"</script>"
],
"text/plain": [
"PreparedData(topic_coordinates= x y topics cluster Freq\n",
"topic \n",
"2 0.124959 0.052005 1 1 34.073612\n",
"0 -0.009108 -0.117322 2 1 33.859391\n",
"1 -0.115850 0.065317 3 1 32.066997, topic_info= Term Freq Total Category logprob loglift\n",
"151 funding 19.000000 19.000000 Default 30.0000 30.0000\n",
"46 enrollment 18.000000 18.000000 Default 29.0000 29.0000\n",
"56 sponsor 21.000000 21.000000 Default 28.0000 28.0000\n",
"18 recruitment 17.000000 17.000000 Default 27.0000 27.0000\n",
"24 pi 11.000000 11.000000 Default 26.0000 26.0000\n",
".. ... ... ... ... ... ...\n",
"61 new 2.982376 7.676073 Topic3 -5.1545 0.1920\n",
"4 patients 3.450958 14.649983 Topic3 -5.0086 -0.3084\n",
"223 patient 2.643624 6.247916 Topic3 -5.2751 0.2772\n",
"85 low 2.317066 11.887986 Topic3 -5.4070 -0.4979\n",
"147 changes 2.309745 4.156629 Topic3 -5.4101 0.5498\n",
"\n",
"[186 rows x 6 columns], token_table= Topic Freq Term\n",
"term \n",
"58 2 0.605562 accrual\n",
"58 3 0.385358 accrual\n",
"455 1 0.959249 achieved\n",
"153 1 0.576575 another\n",
"153 3 0.288287 another\n",
"... ... ... ...\n",
"494 3 0.962652 us\n",
"140 2 0.949880 wait\n",
"143 1 0.390273 withdrawn\n",
"143 2 0.520364 withdrawn\n",
"143 3 0.130091 withdrawn\n",
"\n",
"[194 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 2])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/alejandra/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n"
]
}
],
"source": [
"# VISUALIZE THE TOPICS\n",
"pyLDAvis.enable_notebook()\n",
"p = pyLDAvis.gensim_models.prepare(ldamodel, doc_term_matrix, dictionary)\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "4966dfd0-1e9f-41f0-9b7c-23d8abde57d7",
"metadata": {},
"outputs": [],
"source": [
"pyLDAvis.save_html(p, 'lda.html')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "b27ab81d-20e6-45bc-95da-76c8d66b7332",
"metadata": {},
"outputs": [],
"source": [
"import imageio.v2 as imageio\n",
"import os\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "0b6383cd-4661-40ab-93bc-065366da3a8f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['topic1.png', 'topic2.png', 'topic3.png']\n"
]
}
],
"source": [
"filepath = os.listdir('Images/Gif')\n",
"print(filepath)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "9b2d8cd7-61aa-4905-9f95-95b947949ae1",
"metadata": {},
"outputs": [],
"source": [
"image_path = [os.path.join('Images/Gif',file) for file in filepath]\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "ac3ed395-95d8-4513-bc67-d8831a72e7cb",
"metadata": {},
"outputs": [],
"source": [
"images = []\n",
"for img in image_path:\n",
" images.append(imageio.imread(img))\n",
" \n",
"imageio.mimwrite('Images/LDAvis.gif', images, fps=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "804a3698-0846-482a-90a6-3d914f7eb6af",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "mlenv",
"language": "python",
"name": "mlenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}