Use of machine learning for real-time antibiotic treatment adjustment in high-risk patients with CRGNB infection.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 11, 2025

Abstract

BACKGROUND: Infections caused by carbapenem resistant gram-negative bacilli (CRGNB) are associated with high mortality and pose a great challenge for clinical treatment. We aim to identify patients at high risk for CRGNB as early as possible and alert clinicians to make timely antibiotic treatment adjustments.

Authors

  • Qian Zhang
    The Neonatal Intensive Care Unit, Peking Union Medical College Hospital, Peking, China.
  • Yejun Wu
    School of Computer Science, Wuhan University, Wuhan, Hubei Province, China; Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, Hubei Province, China.
  • Lei Zhao
    Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
  • Jiao Xie
    Health Management Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
  • Haotian Mao
    Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, Hubei Province, China.
  • Chi Guo
    Global Navigation Satellite System Research Center, Wuhan University, Wuhan, China.
  • Xin Zheng
    Department of Clinical Laboratory, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China. Electronic address: dearjanna@126.com.

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