Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.

Journal: Resuscitation
PMID: 30664917

Abstract

BACKGROUND: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center.

Authors

  • Stig Nikolaj Blomberg
    Emergency Medical Services Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark. Electronic address: Stig.Nikolaj.Fasmer.Blomberg@regionh.dk.
  • Fredrik Folke
    Copenhagen Emergency Medical Services, University of Copenhagen, Ballerup, Denmark.
  • Annette Kjær Ersbøll
    National Institute of Public Health, University of Southern Denmark, Denmark.
  • Helle Collatz Christensen
    Emergency Medical Services Copenhagen, Denmark.
  • Christian Torp-Pedersen
    From the Danish Heart Foundation, Copenhagen, Denmark (P.W.H., T.S.G.S., E.L.F., G.H.G.); DTU Compute, Technical University of Denmark, Lyngby (L.C.); The Heart Centre, Rigshospitalet (E.L.F., L.K.), and Department of Clinical Medicine (G.H.G.), University of Copenhagen, Denmark; Institute of Health, Science and Technology, Aalborg University, Denmark (C.T.-P.); The National Institute of Public Health, University of Southern Denmark, Copenhagen (G.H.G.); University of Copenhagen, Denmark; and Department of Medicine, Section of Cardiology, Glostrup Hospital, University of Copenhagen, Denmark (C.A.).
  • Michael R Sayre
    Department of Emergency Medicine, University of Washington, United States.
  • Catherine R Counts
    Department of Emergency Medicine, University of Washington, United States.
  • Freddy K Lippert
    Emergency Medical Services Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark.

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