README.md · semaj83/ctmatch_classification at 76aeec3a9e337f33e9fd2added41d85d0a616e97

metadata

license: mit
task_categories:
  - text-classification
tags:
  - medical
size_categories:
  - 10K<n<100K

CTMatch Dataset

This is a combined 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).

These 2 datasets contain no patient identifying information are openly available in raw forms:

Additionally, for the IR task, other feature representations of the unlabelled 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