Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department.

Journal: BMC emergency medicine
Published Date: Dec 18, 2024

Abstract

BACKGROUND: Accurate triage is required for efficient allocation of resources and to decrease patients' length of stay. Triage decisions are often subjective and vary by provider, leading to patients being over-triaged or under-triaged. This study developed machine learning models that incorporated natural language processing (NLP) to predict patient disposition. The models were assessed by comparing their performance with the judgements of emergency physicians (EPs).

Authors

  • Yu-Hsin Chang
    Department of Emergency Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist, Taichung City, 40447, Taiwan.
  • Ying-Chen Lin
    Institute of Information Science and Engineering, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist, Hsinchu City, 300093, Taiwan.
  • Fen-Wei Huang
    Department of Emergency Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist, Taichung City, 40447, Taiwan.
  • Dar-Min Chen
    Department of Emergency Medicine, Asia University Hospital, No. 222, Fuxin Rd., Wufeng Dist, Taichung City, 413505, Taiwan.
  • Yu-Ting Chung
    Department of Emergency Medicine, Asia University Hospital, No. 222, Fuxin Rd., Wufeng Dist, Taichung City, 413505, Taiwan.
  • Wei-Kung Chen
    Department of Emergency Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist, Taichung City, 40447, Taiwan. ercwk@mail.cmuh.org.tw.
  • Charles C N Wang
    Department of Bioinformatics and Medical Engineering, Asia University, No. 500, Liufeng Rd., Wufeng Dist, Taichung City, 413305, Taiwan. cnwang@asia.edu.tw.

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