Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Blomberg, S. N., Folke, F., Ersbøll, A. K., Christensen, H. C., Torp-Pedersen, C., Sayre, M. R., Counts, C. R., & Lippert, F. K. Resuscitation, 138:322–329, May, 2019.
doi  abstract   bibtex   
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 \textless 0.001) with lower specificity (98.8% vs. 97.3%, p \textless 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9% vs. 33.0%, p \textless 0.001). Time-to-recognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p \textless 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.
@article{blomberg_machine_2019,
	title = {Machine learning as a supportive tool to recognize cardiac arrest in emergency calls},
	volume = {138},
	issn = {1873-1570},
	doi = {10.1016/j.resuscitation.2019.01.015},
	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 {\textless} 0.001) with lower specificity (98.8\% vs. 97.3\%, p {\textless} 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9\% vs. 33.0\%, p {\textless} 0.001). Time-to-recognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p {\textless} 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.},
	language = {eng},
	journal = {Resuscitation},
	author = {Blomberg, Stig Nikolaj and Folke, Fredrik and Ersbøll, Annette Kjær and Christensen, Helle Collatz and Torp-Pedersen, Christian and Sayre, Michael R. and Counts, Catherine R. and Lippert, Freddy K.},
	month = may,
	year = {2019},
	pmid = {30664917},
	keywords = {Artificial intelligence, Cardiopulmonary resuscitation, Detection time, Dispatch-assisted cardiopulmonary resuscitation, Emergency medical services, Machine learning, Out-of-hospital cardiac arrest},
	pages = {322--329}
}
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