Datasets:
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README.md
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**CTMatch Dataset**
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This is a combied set of 2 labelled datasets of
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These have been processed using ctproc, and in this state can be used by various tokenizers for fine-tuning (see ctmatch for examples).
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Additionally, for the IR task, other feature representations of the documents have been created, each of these 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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see repo for more information
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https://github.com/semajyllek/ctmatch
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**CTMatch Dataset**
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This is a combied set of 2 labelled datasets of:
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`topic (patient descriptions), doc (clinical trials documents - selected fields), and label ({0, 1, 2})` triples, in jsonl format.
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(Somewhat of a duplication of some of the `ir_dataset` also available on HF.)
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These have been processed using ctproc, and in this state can be used by various tokenizers for fine-tuning (see ctmatch for examples).
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Additionally, for the IR task, other feature representations of the documents have been created, each of these 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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**see repo for more information**:
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https://github.com/semajyllek/ctmatch
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