Reducing False Alarm Rates in Neonatal Intensive Care: A New Machine Learning Approach.

Journal: Advances in experimental medicine and biology
Published Date: Jan 1, 2020

Abstract

UNLABELLED: In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and caregivers, but may also lead to longer response times in real critical situations. The aim of this project was to reduce the rates of false alarms by employing machine learning algorithms (MLA), which intelligently analyze data stemming from standard physiological monitoring in combination with cerebral oximetry data (in-house built, OxyPrem).

Authors

  • D Ostojic
    Biomedical Optics Research Laboratory (BORL), University of Zurich, Zurich, Switzerland. Daniel.Ostojic@usz.ch.
  • S Guglielmini
    Biomedical Optics Research Laboratory (BORL), University of Zurich, Zurich, Switzerland.
  • V Moser
    CSEM, Neuchâtel, Switzerland.
  • J C Fauchère
    Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • H U Bucher
    Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • D Bassler
    Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • M Wolf
    Biomedical Optics Research Laboratory (BORL), University of Zurich, Zurich, Switzerland.
  • S Kleiser
    Biomedical Optics Research Laboratory (BORL), University of Zurich, Zurich, Switzerland.
  • F Scholkmann
    Biomedical Optics Research Laboratory (BORL), University of Zurich, Zurich, Switzerland.
  • T Karen
    Biomedical Optics Research Laboratory (BORL), University of Zurich, Zurich, Switzerland.

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