Enabling machine learning models in alarm fatigue research: Creation of a large relevance-annotated oxygen saturation alarm data set.

Journal: Computers in biology and medicine
PMID: 39454525

Abstract

BACKGROUND: Too many unnecessary alarms in the intensive care unit are one of the main reasons for alarm fatigue: Medical staff is overburdened and fails to respond appropriately. This endangers both patients and staff. Currently, there are no algorithms that can determine which alarms are clinically relevant and which are not.

Authors

  • Jonas Chromik
  • Anne Rike Flint
    Institute of Medical Informatics at Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Berlin, Germany. Electronic address: anne-rike.flint@charite.de.
  • Mona Prendke
    Institute of Medical Informatics at Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Berlin, Germany. Electronic address: mona.prendke@charite.de.
  • Bert Arnrich
    Hasso Plattner Institute, University of Potsdam, Germany.
  • Akira-Sebastian Poncette
    Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany.

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