Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients.

Journal: International journal of medical informatics
PMID: 39823968

Abstract

BACKGROUND: Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. Traditional diagnostic approaches, such as blood cultures, are time-consuming. This limitation has encouraged the exploration of rapid prediction methodologies. Cellular Population Data (CPD), which provides detailed insights into white blood cell morphology and functionality, is a promising technique for the early detection of bacteremia.

Authors

  • Wei-Hsun Chen
    Department of Emergency Medicine, China Medical University Hospital, Taichung, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.
  • Yu-Hsin Chang
    Department of Emergency Medicine, China Medical University Hospital, No. 2, Yude Rd., North Dist, Taichung City, 40447, Taiwan.
  • Chiung-Tzu Hsiao
    Departments of Laboratory Medicine, China Medical University Hospital, China.
  • Po-Ren Hsueh
    Department of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan; Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan. Electronic address: hsporen@gmail.com.
  • Hong-Mo Shih
    Department of Emergency Medicine, China Medical University Hospital, Taichung, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan. Electronic address: homoe042002@hotmail.com.

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