Machine Learning-Based Prediction Models for Clostridioides difficile Infection: A Systematic Review.

Journal: Clinical and translational gastroenterology
PMID: 38661188

Abstract

INTRODUCTION: Despite research efforts, predicting Clostridioides difficile incidence and its outcomes remains challenging. The aim of this systematic review was to evaluate the performance of machine learning (ML) models in predicting C. difficile infection (CDI) incidence and complications using clinical data from electronic health records.

Authors

  • Raseen Tariq
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN.
  • Sheza Malik
    Internal Medicine, Rochester General Hospital, Rochester, New York, USA.
  • Renisha Redij
    GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
  • Shivaram Arunachalam
    Mayo Clinic, Rochester, Minnesota, USA.
  • William A Faubion
    Mayo Clinic, Arizona, Scottsdale, USA.
  • Sahil Khanna
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN.

Keywords

External Resources

Popular Topics
Recent Journals