Researchers have developed artificial intelligence (AI) models that can scrutinize electronic health records (EHR) and electrocardiograms to identify individuals in the general population at elevated risk for sudden cardiac arrest — a condition that causes more than 400,000 U.S. deaths annually and has a survival rate of only 10%. 

The finding represents a significant advance in predicting a largely unpredictable medical emergency that often strikes people with no known heart disease.  

"Using artificial intelligence applications and health records data, the prediction of cardiac arrest in the general population is feasible,” said Dr. Neal Chatterjee, the study’s lead investigator and a cardiologist at the University of Washington School of Medicine.   

JACC: Advances, a journal of the American College of Cardiology, published the paper today. Other co-senior authors are from Massachusetts General Hospital and the Broad Institute of MIT and Harvard.  

Kuo-Chu Chang, Professor, Systems Engineering and Operations Research (SEOR), College of Engineering and Computing (CEC), received funding for the project: “PRECISION: Predictive Resilient Chain Intelligence with Secure Integrated Optimization Networks.”