Machine learning for adverse event prediction in outpatient parenteral antimicrobial therapy: a scoping review.

Journal: The Journal of antimicrobial chemotherapy
PMID: 39351986

Abstract

OBJECTIVE: This study aimed to conduct a scoping review of machine learning (ML) techniques in outpatient parenteral antimicrobial therapy (OPAT) for predicting adverse outcomes and to evaluate their validation, implementation and potential barriers to adoption.

Authors

  • Douglas W Challener
    Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, USA.
  • Madiha Fida
    Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, USA.
  • Peter Martin
    Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
  • Christina G Rivera
    Department of Pharmacy, Mayo Clinic, Rochester, MN, USA.
  • Abinash Virk
    Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, USA.
  • Lorne W Walker
    Division of Pediatric Infectious Diseases, Oregon Health and Sciences University, Portland, Oregon, USA.

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