MedNLI - A Natural Language Inference Dataset For The Clinical Domain

State of the art models using deep neural networks have become very good in
learning an accurate mapping from inputs to outputs. However, they still lack
generalization capabilities in conditions that differ from the ones
encountered during training. This is even more challenging in specialized, and
knowledge intensive domains, where training data is limited. To address this
gap, we introduce MedNLI - a dataset annotated by doctors, performing a
natural language inference task (NLI), grounded in the medical history of
patients. As the source of premise sentences, we used the MIMIC-III. More
specifically, to minimize the risks to patient privacy, we worked with
clinical notes corresponding to the deceased patients. The clinicians in our
team suggested the Past Medical History to be the most informative section of
a clinical note, from which useful inferences can be drawn about the patient.