Datasets:

semaj83
/

ctmatch_classification

@@ -4,7 +4,10 @@ license: mit
 **CTMatch Dataset**
-This is a combied set of 2 labelled datasets of topic (patient descriptions), doc (clinical trials documents - selected fields), and label ({0, 1, 2}) triples, in jsonl format.
 These have been processed using ctproc, and in this state can be used by various tokenizers for fine-tuning (see ctmatch for examples).
@@ -19,23 +22,23 @@ These 2 datasets contain no patient identifying information are openly available
 Additionally, for the IR task, other feature representations of the documents have been created, each of these has exactly 374648 lines of corresponding data:
-doc_texts.csv
 - texts extracted from processed documents using several fields including eligbility min and max age, and eligbility criteria, structured as this example from NCT00000102:
   "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"
-doc_categories.csv:
 - 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:
 cancer,cardiac,endocrine,gastrointestinal,genetic,healthy,infection,neurological,other,pediatric,psychological,pulmonary,renal,reproductive
-doc_embeddings.csv:
 - 1 x 384 vectors of embeddings taken from last hidden state of CTMatch.embedding_model.encode(doc_text) using SentenceTransformers
-index2docid.csv
 - simple mapping of index to NCTID's for filtering/reference throughout IR program, corresponding to vector, texts representation order
-see repo for more information:
 https://github.com/semajyllek/ctmatch

 **CTMatch Dataset**
+This is a combied set of 2 labelled datasets of:
+`topic (patient descriptions), doc (clinical trials documents - selected fields), and label ({0, 1, 2})` triples, in jsonl format.
+(Somewhat of a duplication of some of the `ir_dataset` also available on HF.)
 These have been processed using ctproc, and in this state can be used by various tokenizers for fine-tuning (see ctmatch for examples).
 Additionally, for the IR task, other feature representations of the documents have been created, each of these has exactly 374648 lines of corresponding data:
+`doc_texts.csv`
 - texts extracted from processed documents using several fields including eligbility min and max age, and eligbility criteria, structured as this example from NCT00000102:
   "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"
+`doc_categories.csv`:
 - 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:
 cancer,cardiac,endocrine,gastrointestinal,genetic,healthy,infection,neurological,other,pediatric,psychological,pulmonary,renal,reproductive
+`doc_embeddings.csv`:
 - 1 x 384 vectors of embeddings taken from last hidden state of CTMatch.embedding_model.encode(doc_text) using SentenceTransformers
+`index2docid.csv`:
 - simple mapping of index to NCTID's for filtering/reference throughout IR program, corresponding to vector, texts representation order
+**see repo for more information**:
 https://github.com/semajyllek/ctmatch