Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study.

Journal: Journal of medical Internet research
PMID: 39451101

Abstract

BACKGROUND: Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to either adverse outcomes in a patient who did not receive appropriate care in the case of undertriage or a waste of hospital resources in the case of overtriage. Recent advances in machine learning algorithms allow for the quantification of variables important to under- and overtriage.

Authors

  • Sage Wyatt
    Department of Global Public Health, Faculty of Medicine, University of Bergen, Bergen, Norway.
  • Dagfinn Lunde Markussen
    Department of Emergency Medicine, Haukeland University Hospital, Bergen, Norway.
  • Mounir Haizoune
    Helse Vest IKT, Bergen, Norway.
  • Anders Strand Vestbø
    Department of Research and Development, Haukeland University Hospital, Bergen, Norway.
  • Yeneabeba Tilahun Sima
    Department of Global Public Health, Faculty of Medicine, University of Bergen, Bergen, Norway.
  • Maria Ilene Sandboe
    Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
  • Marcus Landschulze
    Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences (HVL), Bergen, Norway.
  • Hauke Bartsch
    Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway.
  • Christopher Martin Sauer
    Department of Haematology & Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, Essen, Germany; Laboratory for Clinical Research and Real-World Evidence, Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. Electronic address: christopher.sauer@uk-essen.de.

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