Predicting Clostridioides difficile infection outcomes with explainable machine learning.

Journal: EBioMedicine
PMID: 39018757

Abstract

BACKGROUND: Clostridioides difficile infection results in life-threatening short-term outcomes and the potential for subsequent recurrent infection. Predicting these outcomes at diagnosis, when important clinical decisions need to be made, has proven to be a difficult task.

Authors

  • Gregory R Madden
    Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA; Office of Hospital Epidemiology/Infection Prevention & Control, University of Virginia School of Medicine, Charlottesville, VA, USA. Electronic address: grm7q@virginia.edu.
  • Rachel H Boone
    Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.
  • Emmanuel Lee
    University of Virginia School of Medicine, Charlottesville, VA, USA.
  • Costi D Sifri
    Division of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA; Office of Hospital Epidemiology/Infection Prevention & Control, University of Virginia School of Medicine, Charlottesville, VA, USA.
  • William A Petri
    Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, Virginia, USA.

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