Datasets:
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README.md
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#### CSIRO: https://data.csiro.au/collection/csiro:17152
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#### CSIRO: https://data.csiro.au/collection/csiro:17152
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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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