A machine-learning module that analyzes electrocardiogram signals can help physicians detect pulmonary embolisms, according to a study published Nov. 25 in European Heart Journal-Digital Health.
Scientists at the New York City-based Icahn School of Medicine at Mount Sinai tested various algorithms on 21,183 Mount Sinai Health System patients who showed moderate to highly suspicious signs of having pulmonary embolisms, or blood clots that can cut off circulation to the lungs.
The scientists found that machine-learning algorithms, designed to exploit a combination of EKG and EHR data, were better at identifying specific pulmonary embolism cases than the Wells' Criteria Revised Geneva Score and three other currently used screening tests.
The fusion model, which combined the best-performing EKG algorithm with the best-performing EHR algorithm, was between 15 and 30 percent more effective at accurately screening acute embolism cases.