Clinical risk prediction using language models: benefits and considerations.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Sep 1, 2024

Abstract

OBJECTIVE: The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks.

Authors

  • Angeela Acharya
    George Mason University, Fairfax, VA, United States.
  • Sulabh Shrestha
    George Mason University, Fairfax, VA, United States.
  • Anyi Chen
    Staten Island Performing Provider System, Staten Island, NY, United States.
  • Joseph Conte
    Staten Island Performing Provider System, Staten Island, NY, United States.
  • Sanja Avramovic
    Deprtment of Health Administration and Policy, College of Public Health, George Mason University, Fairfax, Virginia, United States.
  • Siddhartha Sikdar
    George Mason University, Fairfax, VA, United States.
  • Antonios Anastasopoulos
    George Mason University, Fairfax, VA, United States.
  • Sanmay Das
    George Mason University, Fairfax, VA, United States.

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