🩺 Can artificial intelligence beat our gold standard methods for detecting heart attacks? ⚡️💪
A new machine learning model uses electrocardiogram readings to diagnose and classify heart attacks faster and more accurately than current approaches, according to a study led by University of Pittsburgh researchers that published today in"When a patient comes into the hospital with chest pain, the first question we ask is whether the patient is having a heart attack or not.
The model was developed by co-author Ervin Sejdic, Ph.D., associate professor at The Edward S. Rogers Department of Electrical and Computer Engineering at the University of Toronto and the Research Chair in Artificial Intelligence for Health Outcomes at North York General Hospital in Toronto, with ECGs from 4,026 patients with chest pain at three hospitals in Pittsburgh. The model was then externally validated with 3,287 patients from a different hospital system.
"In our wildest dreams, we hoped to match the accuracy of HEART, but we were surprised to find that our machine learning model based solely on ECGAccording to co-author Christian Martin-Gill, M.D., M.P.H., chief of the Emergency Medical Services division at UPMC, the algorithm will help EMS personnel and emergency department providers identify people having a heart attack and those with reduced blood flow to the heart in a much more robust way compared with traditional ECG analysis.
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