Language Inference in Medicine
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 released MedNLI – a dataset annotated by doctors, performing a natural language inference (NLI) task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. Stanford’s NLI dataset) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies).
The models and data for MedNLI were created using MIT’s Medical Information Mart for Intensive Care (MIMIC) database and are freely available through their derived data repository. The ability to perform NLI in the medical domain can have interesting applications such as introducing automation in clinical trial screening, checking for compliance of clinical care guidelines, and more.
I use machine learning and natural language processing to solve problems with clinical data.
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