Health system-scale language models are all-purpose prediction engines

Abstract Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment 1–3 . Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing 4,5 to train a large…

• WM
  W. M. Keck Foundation
• NC
  National Cancer Institute

  Award: 3P30CA016087-41S1