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Emergency Medical Dispatch

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

Resuscitation 30664917
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 lear...
Aged Aged, 80 and over Cardiopulmonary Resuscitation Emergency Medical Dispatch Emergency Medical Service Communication Systems Emergency Medical Services Female Humans Machine Learning Male Middle Aged Out-of-Hospital Cardiac Arrest Retrospective Studies ROC Curve Time Factors
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Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial.

JAMA network open 33404620
IMPORTANCE: Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.
Aged Denmark Double-Blind Method Emergency Medical Dispatch Female Humans Machine Learning Male Out-of-Hospital Cardiac Arrest
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Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).

Scientific reports 40274919
The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimately saving lives. Delays in ambulance arrival can have serious consequences for patient health and s...
Ambulances Deep Learning Emergency Medical Dispatch Emergency Medical Services Humans Neural Networks, Computer Support Vector Machine
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