{"url":"/dataset/diskne","name":"DisKnE","full_name":"Disease Knowledge Evaluation","description_markdown":"**DisKnE** is a benchmark for Disease Knowledge Evaluation built from MedNLI and MEDIQA-NLI. This benchmark is constructed to specifically test the medical reasoning capabilities of ML models, such as mapping symptoms to diseases.\r\n\r\nThe dataset was built by annotating each positive MedNLI example with the types of medical reasoning that are needed. Negative examples were created by corrupting these positive examples in an adversarial way. Furthermore, the training-test splits are defined per disease, ensuring that no knowledge about test diseases can be learned from the training data.","description_withheld":null,"homepage":"https://github.com/israa-alghanmi/DisKnE","introduced_date":"2021-06-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/probing-pre-trained-language-models-for","title":"Probing Pre-Trained Language Models for Disease Knowledge","first_author":"Israa Alghanmi","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["DisKnE"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}