A large language model for electronic health records

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language…

• PO
  Patient-Centered Outcomes Research Institute

  Award: ME-2018C3-14754
• UO
  University of Florida Health
• CA
  Clinical and Translational Science Institute, University of Florida
• NI
  National Institute on Aging

  Awards: R56AG069880, R21AG062884, NIA R56AG069880
• NC
  National Cancer Institute

  Awards: R56AG069880, 1R01CA246418, 1R01CA246418 R01