Pretrained patient trajectories for adverse drug event prediction using common data model-based electronic health records.

Journal: Communications medicine
Published Date: Jun 13, 2025

Abstract

BACKGROUND: Pretraining electronic health record (EHR) data using language models has enhanced performance across various medical tasks. Despite the potential of EHR pretraining models, predicting adverse drug events (ADEs) using EHR pretraining models has not been explored.

Authors

  • Junmo Kim
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea.
  • Joo Seong Kim
    Department of Internal Medicine, Dongguk University College of Medicine, Dongguk University Ilsan Hospital, Goyang, Korea.
  • Ji-Hyang Lee
    Drug Safety Center, Seoul National University Hospital, Seoul, Republic of Korea.
  • Min-Gyu Kim
  • Taehyun Kim
    Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Gyeonggi, 13620, Korea.
  • Chaeeun Cho
    Department of Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
  • Rae Woong Park
    Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.
  • Kwangsoo Kim
    Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.

Keywords

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