Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jul 1, 2020

Abstract

OBJECTIVE: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting.

Authors

  • Stefanie Jauk
    CBmed, Graz, Austria.
  • Diether Kramer
    Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Graz, Austria.
  • Birgit Großauer
    Department of Internal Medicine, Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes) LKH Graz II, Graz, Austria.
  • Susanne Rienmüller
    Department of Internal Medicine, Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes) LKH Graz II, Graz, Austria.
  • Alexander Avian
    Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
  • Andrea Berghold
    Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
  • Werner Leodolter
    Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Graz, Austria.
  • Stefan Schulz
    Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.

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