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Resuscitation. 2019 May;138:322-329. doi: 10.1016/j.resuscitation.2019.01.015. Epub 2019 Jan 18.

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

Author information

1
Emergency Medical Services Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark. Electronic address: Stig.Nikolaj.Fasmer.Blomberg@regionh.dk.
2
Emergency Medical Services Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark; Department of Cardiology, Gentofte University Hospital, Denmark.
3
National Institute of Public Health, University of Southern Denmark, Denmark.
4
Emergency Medical Services Copenhagen, Denmark.
5
Department of Clinical Epidemiology, Aalborg University Hospital, Denmark; Department of Health Science and Technology, Aalborg University, Denmark.
6
Department of Emergency Medicine, University of Washington, United States.
7
Emergency Medical Services Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark.

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.

METHODS:

For all incidents responded to by Emergency Medical Dispatch Center Copenhagen in 2014, the associated call was retrieved. A machine learning framework was trained to recognize cardiac arrest from the recorded calls. Sensitivity, specificity, and positive predictive value for recognizing out-of-hospital cardiac arrest were calculated. The performance of the machine learning framework was compared to the actual recognition and time-to-recognition of cardiac arrest by medical dispatchers.

RESULTS:

We examined 108,607 emergency calls, of which 918 (0.8%) were out-of-hospital cardiac arrest calls eligible for analysis. Compared with medical dispatchers, the machine learning framework had a significantly higher sensitivity (72.5% vs. 84.1%, p < 0.001) with lower specificity (98.8% vs. 97.3%, p < 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9% vs. 33.0%, p < 0.001). Time-to-recognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p < 0.001).

CONCLUSIONS:

A machine learning framework performed better than emergency medical dispatchers for identifying out-of-hospital cardiac arrest in emergency phone calls. Machine learning may play an important role as a decision support tool for emergency medical dispatchers.

KEYWORDS:

Artificial intelligence; Cardiopulmonary resuscitation; Detection time; Dispatch-assisted cardiopulmonary resuscitation; Emergency medical services; Machine learning; Out-of-hospital cardiac arrest

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