Artificial Intelligence Based Cardiac Event Predictor Systems and Methods

A method and system for determining cardiac disease risk from electrocardiogram trace data is provided. The method includes receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads. One...

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Hauptverfasser: Pfeifer, John, Zimmerman, Noah, Haggerty, Christopher, Nemani, Arun, Morland, Thomas, Steinhubl, Steve, Jing, Linyuan, Ulloa-Cerna, Alvaro, Lee, Greg, Fornwalt, Brandon, Chen, Ruijun, Raghunath, Sushravya
Format: Patent
Sprache:eng
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Zusammenfassung:A method and system for determining cardiac disease risk from electrocardiogram trace data is provided. The method includes receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads. One or more leads of the plurality of leads that are derivable from a combination of other leads of the plurality of leads are identified, and a portion of the electrocardiogram trace data does not include electrocardiogram trace data of the one or more leads. The portion of the electrocardiogram data is provided to a trained machine learning model, to evaluate the portion of the electrocardiogram trace data with respect to one or more cardiac disease states. A risk score reflecting a likelihood of the patient being diagnosed with a cardiac disease state within a predetermined period of time is generated by the trained machine learning model based on the evaluation.