Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?

Journal: The Journal of international medical research
PMID: 39552114

Abstract

OBJECTIVE: To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches.

Authors

  • Pongsathorn Piebpien
    Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
  • Amarit Tansawet
    Department of Surgery, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand. Electronic address: amarit@nmu.ac.th.
  • Oraluck Pattanaprateep
    Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
  • Anuchate Pattanateepapon
    Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
  • Chumpon Wilasrusmee
    Department of Surgery, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.
  • Gareth J McKay
    Center for Public Health, Royal Victoria Hospital, Queen's University Belfast, Belfast, UK.
  • John Attia
    School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia.
  • Ammarin Thakkinstian
    Departments of Clinical Epidemiology and Biostatistics; and.

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