Tempus Labs, Inc.

Geisinger Clinic

Greg Lee

Alvaro E Ulloa-Cerna

Brandon K Fornwalt

Christopher M Haggerty

John Pfeifer

Arun Nemani

Noah Zimmerman

Steve Steinhubl

Ruijun Chen

Christopher Good

Linyuan Jing

Sushravya Raghunath

Thomas Morland

A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Artificial intelligence based cardiac event predictor systems and methods

A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

ECG-based cardiovascular disease detection systems and related methods

Growing community of inventors

Chicago, IL, United States of America

Greg Lee