Machine learning to recognise cardiac arrest in emergency calls

Nearly 600,000 people in the US and Europe combined sustain an out-of-hospital cardiac arrest (OHCA) annually. OHCA is a life-threatening condition, and in order to improve survival rates, it is imperative that it is recognised rapidly by medical dispatchers. Accurate recognition of OHCA either by a bystander or a dispatcher is essential for initiation of cardiopulmonary resuscitation (CPR).

Despite the fact that improving early recognition is a goal for both the American Heart Association and the Global Resuscitation Alliance, statistics show that nearly 25% of out-of-hospital cardiac arrest cases are not identified by emergency medical dispatchers. This results in the loss of opportunity to provide the emergency caller instructions in cardiopulmonary resuscitation.

Machine learning frameworks are now being used quite often in non-emergency conditions. However, to date, these technologies have not been used to support clinical decision making in an acute medical context. Keeping in mind the low recognition rates for OHCA, there is a possibility that machine learning could be used to improve OHCA recognition and in future, other critical incidents such as stroke, acute myocardial infarction and/or sepsis.