Using machine learning to predict bacteremia in urgent care patients on the basis of triage data and laboratory results.

Journal: The American journal of emergency medicine
PMID: 39243592

Abstract

BACKGROUND: Despite advancements in antimicrobial therapies, bacteremia remains a life-threatening condition. Appropriate antimicrobials must be promptly administered to ensure patient survival. However, diagnosing bacteremia based on blood cultures is time-consuming and not something emergency department (ED) personnel are routinely trained to do.

Authors

  • Chung-Ping Chiu
    Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan. Electronic address: ne6101131@gs.ncku.edu.tw.
  • Hsin-Hung Chou
    Department of Computer Science and Engineering, National Chi Nan University, Nantou, Taiwan. Electronic address: chouhh@ncnu.edu.tw.
  • Peng-Chan Lin
    Internal Medicine Department.
  • Ching-Chi Lee
    Clinical Medicine Research Centre, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan; Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan. Electronic address: chichingbm85@gmail.com.
  • Sun-Yuan Hsieh
    Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.

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