https://phys.org/news/2018-10-language-inference-medicine.html

In order to address these gaps, we worked with the Massachusetts Institute of Technology to build MedNLI, a dataset annotated by doctors, performing a natural language inference (NLI) task and grounded in the medical history of patients. Most importantly, we make it publicly available for researchers to advance natural language processing in medicine.

We worked with the MIT Critical Data research labs to construct a dataset for natural language inference in medicine. We used clinical notes from their "Medical Information Mart for Intensive Care" (MIMIC) database, which is arguably the largest publicly available database of patient records. The clinicians in our team suggested that the past medical history of a patient contains vital information from which useful inferences can be drawn. Therefore, we extracted the past medical history from clinical notes in MIMIC and presented a sentence from this history as a premise to a clinician. They were then requested to use their medical expertise and generate three sentences: a sentence that was definitely true about the patient, given the premise; a sentence that was definitely false, and finally a sentence that could possibly be true.