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{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"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>id</th>\n",
" <th>nct_id</th>\n",
" <th>mesh_term</th>\n",
" <th>downcase_mesh_term</th>\n",
" <th>mesh_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>336369685</td>\n",
" <td>NCT04016870</td>\n",
" <td>Infections</td>\n",
" <td>infections</td>\n",
" <td>mesh-ancestor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>336369788</td>\n",
" <td>NCT03266874</td>\n",
" <td>Necrosis</td>\n",
" <td>necrosis</td>\n",
" <td>mesh-list</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>336369897</td>\n",
" <td>NCT02743455</td>\n",
" <td>Fever</td>\n",
" <td>fever</td>\n",
" <td>mesh-list</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>336370004</td>\n",
" <td>NCT01683877</td>\n",
" <td>Neoplasms</td>\n",
" <td>neoplasms</td>\n",
" <td>mesh-ancestor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>336370095</td>\n",
" <td>NCT01268579</td>\n",
" <td>Carcinoma</td>\n",
" <td>carcinoma</td>\n",
" <td>mesh-list</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id nct_id mesh_term downcase_mesh_term mesh_type\n",
"0 336369685 NCT04016870 Infections infections mesh-ancestor\n",
"1 336369788 NCT03266874 Necrosis necrosis mesh-list\n",
"2 336369897 NCT02743455 Fever fever mesh-list\n",
"3 336370004 NCT01683877 Neoplasms neoplasms mesh-ancestor\n",
"4 336370095 NCT01268579 Carcinoma carcinoma mesh-list"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv('file_db/browse_conditions.txt', delimiter='|') # Use the appropriate delimiter if not tab-separated\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"files_to_keep = [\"brief_summaries\", \"interventions\", \"keywords\", \"browse_conditions\"]\n",
"\n",
"# maybe \"study_references\" \"sponsors\" \"overall_officials\" \"pending_results\" \"outcome_analyses\" \"provided_documents\" \"reported_event_totals\" \"responsible_parties\"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>nct_id</th>\n",
" <th>summary</th>\n",
" <th>intervention_name</th>\n",
" <th>intervention_type</th>\n",
" <th>intervention_description</th>\n",
" <th>keywords</th>\n",
" <th>desease_condition</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NCT03569293</td>\n",
" <td>The objective of this study is to assess the e...</td>\n",
" <td>[Placebo for Upadacitinib, Upadacitinib]</td>\n",
" <td>Drug</td>\n",
" <td>Tablets taken orally once a day</td>\n",
" <td>[Atopic Dermatitis, Upadacitinib]</td>\n",
" <td>[dermatitis, atopic, dermatitis, eczema, skin ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NCT03556839</td>\n",
" <td>The study will integrate the efficacy of combi...</td>\n",
" <td>[Atezolizumab, Bevacizumab, Cisplatin/Carbopla...</td>\n",
" <td>Drug</td>\n",
" <td>Intravenous Infusion</td>\n",
" <td>[Cervix, Carcinoma, Atezolizumab]</td>\n",
" <td>[carcinoma, neoplasms, glandular and epithelia...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NCT03526874</td>\n",
" <td>Migraine affects 10-28% of children and adoles...</td>\n",
" <td>[Lidocaine 4% Topical Application Cream [LMX 4...</td>\n",
" <td>Drug</td>\n",
" <td>Run-in Step: All subjects receive 32 mg (4 cm ...</td>\n",
" <td>[Episodic Migraine, Headache, Nerve Block, Pai...</td>\n",
" <td>[pain, migraine disorders, headache, headache ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>NCT03526835</td>\n",
" <td>This is a Phase 1/2 open-label, multi-center, ...</td>\n",
" <td>[MCLA-158, MCLA-158 +Pembrolizumab]</td>\n",
" <td>Drug</td>\n",
" <td>full-length IgG1 bispecific antibody targeting...</td>\n",
" <td>[Bispecific antibody, First-in-human, MCLA-158...</td>\n",
" <td>[squamous cell carcinoma of head and neck, neo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>NCT02272751</td>\n",
" <td>This study will aim to compare the effects of ...</td>\n",
" <td>[Exercise, Relaxation]</td>\n",
" <td>Behavioral</td>\n",
" <td>The Exercise intervention will consist of aero...</td>\n",
" <td>[cancer survivorship, exercise, relaxation, mi...</td>\n",
" <td>[lymphoma, neoplasms by histologic type, neopl...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" nct_id summary \\\n",
"0 NCT03569293 The objective of this study is to assess the e... \n",
"2 NCT03556839 The study will integrate the efficacy of combi... \n",
"6 NCT03526874 Migraine affects 10-28% of children and adoles... \n",
"9 NCT03526835 This is a Phase 1/2 open-label, multi-center, ... \n",
"11 NCT02272751 This study will aim to compare the effects of ... \n",
"\n",
" intervention_name intervention_type \\\n",
"0 [Placebo for Upadacitinib, Upadacitinib] Drug \n",
"2 [Atezolizumab, Bevacizumab, Cisplatin/Carbopla... Drug \n",
"6 [Lidocaine 4% Topical Application Cream [LMX 4... Drug \n",
"9 [MCLA-158, MCLA-158 +Pembrolizumab] Drug \n",
"11 [Exercise, Relaxation] Behavioral \n",
"\n",
" intervention_description \\\n",
"0 Tablets taken orally once a day \n",
"2 Intravenous Infusion \n",
"6 Run-in Step: All subjects receive 32 mg (4 cm ... \n",
"9 full-length IgG1 bispecific antibody targeting... \n",
"11 The Exercise intervention will consist of aero... \n",
"\n",
" keywords \\\n",
"0 [Atopic Dermatitis, Upadacitinib] \n",
"2 [Cervix, Carcinoma, Atezolizumab] \n",
"6 [Episodic Migraine, Headache, Nerve Block, Pai... \n",
"9 [Bispecific antibody, First-in-human, MCLA-158... \n",
"11 [cancer survivorship, exercise, relaxation, mi... \n",
"\n",
" desease_condition \n",
"0 [dermatitis, atopic, dermatitis, eczema, skin ... \n",
"2 [carcinoma, neoplasms, glandular and epithelia... \n",
"6 [pain, migraine disorders, headache, headache ... \n",
"9 [squamous cell carcinoma of head and neck, neo... \n",
"11 [lymphoma, neoplasms by histologic type, neopl... "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_summary = pd.read_csv('file_db/brief_summaries.txt', delimiter='|')\n",
"df_summary = df_summary.rename(columns={'description': 'summary'})\n",
"\n",
"### create and merge intervention ###\n",
"df_intervention = pd.read_csv('file_db/interventions.txt', delimiter='|')\n",
"\n",
"intervention_grouped = df_intervention.groupby('nct_id')['name'].apply(list).reset_index()\n",
"intervention_grouped = intervention_grouped.rename(columns={'name': 'intervention_name'})\n",
"merged_df = pd.merge(\n",
" df_summary[['nct_id', 'summary']], \n",
" intervention_grouped[['nct_id', 'intervention_name']], \n",
" on='nct_id')\n",
"\n",
"df_intervention = df_intervention.rename(columns={'description': 'intervention_description'})\n",
"\n",
"merged_df = pd.merge(\n",
" merged_df,\n",
" df_intervention[['nct_id', 'intervention_type', 'intervention_description']], \n",
" on='nct_id')\n",
"\n",
"### create and merge keywords ###\n",
"df_keyword = pd.read_csv('file_db/keywords.txt', delimiter='|')\n",
"keywords_grouped = df_keyword.groupby('nct_id')['name'].apply(list).reset_index()\n",
"keywords_grouped = keywords_grouped.rename(columns={'name': 'keywords'})\n",
"\n",
"merged_df = pd.merge(\n",
" merged_df,\n",
" keywords_grouped,\n",
" on='nct_id'\n",
")\n",
"\n",
"### create and merge browse conditions\n",
"df_condition = pd.read_csv('file_db/browse_conditions.txt', delimiter='|')\n",
"conditions_grouped = df_condition.groupby('nct_id')['downcase_mesh_term'].apply(list).reset_index()\n",
"conditions_grouped = conditions_grouped.rename(columns={'downcase_mesh_term': 'desease_condition'})\n",
"\n",
"merged_df = pd.merge(\n",
" merged_df,\n",
" conditions_grouped,\n",
" on='nct_id'\n",
")\n",
"\n",
"merged_df = merged_df.drop_duplicates(subset='nct_id')\n",
"\n",
"merged_df.head()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>desease_condition</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[dermatitis, atopic, dermatitis, eczema, skin ...</td>\n",
" <td>nct_id: NCT03569293\\nsummary: The objective of...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[carcinoma, neoplasms, glandular and epithelia...</td>\n",
" <td>nct_id: NCT03556839\\nsummary: The study will i...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>[pain, migraine disorders, headache, headache ...</td>\n",
" <td>nct_id: NCT03526874\\nsummary: Migraine affects...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>[squamous cell carcinoma of head and neck, neo...</td>\n",
" <td>nct_id: NCT03526835\\nsummary: This is a Phase ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>[lymphoma, neoplasms by histologic type, neopl...</td>\n",
" <td>nct_id: NCT02272751\\nsummary: This study will ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" desease_condition \\\n",
"0 [dermatitis, atopic, dermatitis, eczema, skin ... \n",
"2 [carcinoma, neoplasms, glandular and epithelia... \n",
"6 [pain, migraine disorders, headache, headache ... \n",
"9 [squamous cell carcinoma of head and neck, neo... \n",
"11 [lymphoma, neoplasms by histologic type, neopl... \n",
"\n",
" text \n",
"0 nct_id: NCT03569293\\nsummary: The objective of... \n",
"2 nct_id: NCT03556839\\nsummary: The study will i... \n",
"6 nct_id: NCT03526874\\nsummary: Migraine affects... \n",
"9 nct_id: NCT03526835\\nsummary: This is a Phase ... \n",
"11 nct_id: NCT02272751\\nsummary: This study will ... "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Concatenate all columns into one written text\n",
"merged_df['text'] = merged_df.drop(columns=['desease_condition']).apply(lambda row: '\\n'.join([f\"{col}: {val}\" for col, val in row.items()]), axis=1)\n",
"\n",
"# Save the DataFrame to a new CSV file\n",
"merged_df = merged_df[['desease_condition', 'text']]\n",
"merged_df.to_csv('clinical_trials.csv', index=False)\n",
"\n",
"merged_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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