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@@ -7,20 +7,22 @@ license: mit
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  This is a dataset of processed clinical trials documents, somehwat of a duplication of that found in `datasets/ir_datasets`
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  except that these have been preprocessed with ctproc to clean and extract useful fields from the clinical trial documents.
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- Each of the has exactly 374648 lines of corresponding data:
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- `doc_texts.csv`
 
 
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  - texts extracted from processed documents using several fields including eligbility min and max age, and eligbility criteria, structured as this example from NCT00000102:
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  "Inclusion Criteria: diagnosed with Congenital Adrenal Hyperplasia (CAH) normal ECG during baseline evaluation, Exclusion Criteria: history of liver disease, or elevated liver function tests history of cardiovascular disease"
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- `doc_categories.csv`:
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  - 1 x 15 vectors of somewhat arbitrarily chosen topic probabilities (softmax output) generated by zero-shot classification model, CTMatch.category_model(doc['condition']) lexically ordered as such:
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  cancer,cardiac,endocrine,gastrointestinal,genetic,healthy,infection,neurological,other,pediatric,psychological,pulmonary,renal,reproductive
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- `doc_embeddings.csv`:
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  - 1 x 384 vectors of embeddings taken from last hidden state of CTMatch.embedding_model.encode(doc_text) using SentenceTransformers
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- `index2docid.csv`:
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  - simple mapping of index to NCTID's for filtering/reference throughout IR program, corresponding to vector, texts representation order
 
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  This is a dataset of processed clinical trials documents, somehwat of a duplication of that found in `datasets/ir_datasets`
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  except that these have been preprocessed with ctproc to clean and extract useful fields from the clinical trial documents.
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+ Note: They are currently saved as text files because of the downstream task in ctmatch, though in the future they may be converted to .csv.
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+ Each .txt file has exactly 374648 lines of corresponding data:
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+
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+ `doc_texts.txt`
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  - texts extracted from processed documents using several fields including eligbility min and max age, and eligbility criteria, structured as this example from NCT00000102:
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  "Inclusion Criteria: diagnosed with Congenital Adrenal Hyperplasia (CAH) normal ECG during baseline evaluation, Exclusion Criteria: history of liver disease, or elevated liver function tests history of cardiovascular disease"
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+ `doc_categories.txt`:
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  - 1 x 15 vectors of somewhat arbitrarily chosen topic probabilities (softmax output) generated by zero-shot classification model, CTMatch.category_model(doc['condition']) lexically ordered as such:
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  cancer,cardiac,endocrine,gastrointestinal,genetic,healthy,infection,neurological,other,pediatric,psychological,pulmonary,renal,reproductive
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+ `doc_embeddings.txt`:
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  - 1 x 384 vectors of embeddings taken from last hidden state of CTMatch.embedding_model.encode(doc_text) using SentenceTransformers
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+ `index2docid.txt`:
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  - simple mapping of index to NCTID's for filtering/reference throughout IR program, corresponding to vector, texts representation order