Integrating large language models with human expertise for disease detection in electronic health records.

Journal: Computers in biology and medicine
PMID: 40198990

Abstract

OBJECTIVE: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelling of disease outcomes. This study developed an efficient strategy based on advanced large language models to identify multiple conditions from EHR clinical notes.

Authors

  • Jie Pan
  • Seungwon Lee
    Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
  • Cheligeer Cheligeer
    The Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Elliot A Martin
    Centre for Health Informatics, University of Calgary, Calgary, Canada; Health Research Methods and Analytics, Alberta Health Services, Calgary, Canada. Electronic address: eamartin@ucalgary.ca.
  • Kiarash Riazi
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Hude Quan
    Department of Community Health Sciences, University of Calgary, Calgary, Canada.
  • Na Li
    School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.

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