Spaces:
Sleeping
Sleeping
File size: 15,719 Bytes
93e1b64 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 |
{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|