Machine learning as a supportive tool to recognize cardiac arrest in emergency calls
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Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. / Blomberg, Stig Nikolaj; Folke, Fredrik; Ersbøll, Annette Kjær; Christensen, Helle Collatz; Torp-Pedersen, Christian; Sayre, Michael R.; Counts, Catherine R.; Lippert, Freddy K.
In: Resuscitation, Vol. 138, 2019, p. 322-329.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Machine learning as a supportive tool to recognize cardiac arrest in emergency calls
AU - Blomberg, Stig Nikolaj
AU - Folke, Fredrik
AU - Ersbøll, Annette Kjær
AU - Christensen, Helle Collatz
AU - Torp-Pedersen, Christian
AU - Sayre, Michael R.
AU - Counts, Catherine R.
AU - Lippert, Freddy K.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cardiopulmonary resuscitation
KW - Detection time
KW - Dispatch-assisted cardiopulmonary resuscitation
KW - Emergency medical services
KW - Machine learning
KW - Out-of-hospital cardiac arrest
UR - http://www.scopus.com/inward/record.url?scp=85060455459&partnerID=8YFLogxK
U2 - 10.1016/j.resuscitation.2019.01.015
DO - 10.1016/j.resuscitation.2019.01.015
M3 - Journal article
C2 - 30664917
AN - SCOPUS:85060455459
VL - 138
SP - 322
EP - 329
JO - Resuscitation
JF - Resuscitation
SN - 0300-9572
ER -
ID: 239954948