Estimating individual risk of catheter-associated urinary tract infections using explainable artificial intelligence on clinical data.

Journal: American journal of infection control
PMID: 39481544

Abstract

BACKGROUND: Catheter-associated urinary tract infections (CAUTIs) increase clinical burdens. Identifying the high-risk patients is crucial. We aimed to develop and externally validate an explainable, prognostic prediction model of CAUTIs among hospitalized individuals receiving urinary catheterization.

Authors

  • Herdiantri Sufriyana
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Department of Medical Physiology, College of Medicine, University of Nahdlatul Ulama Surabaya, Surabaya 60237, Indonesia. Electronic address: herdiantrisufriyana@unusa.ac.id.
  • Chieh Chen
    Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan.
  • Hua-Sheng Chiu
    Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA. Electronic address: hchiu@bcm.edu.
  • Pavel Sumazin
    Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA. Electronic address: sumazin@bcm.edu.
  • Po-Yu Yang
    School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Jiunn-Horng Kang
    Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, 252 Wuxing St, Xinyi District, 11031, Taipei City, Taiwan.
  • Emily Chia-Yu Su
    Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. emilysu@tmu.edu.tw.

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