Geisinger Clinic

Tempus Labs, Inc.

Linyuan Jing

Brandon K Fornwalt

Christopher M Haggerty

Alvaro E Ulloa-Cerna

Greg Lee

Arun Nemani

John Pfeifer

Ruijun Chen

Noah Zimmerman

Sushravya Raghunath

Steve Steinhubl

Thomas Morland

A method for providing treatment recommendations for a patient to a physician is disclosed. The method includes receiving health information associated with the patient, determining a first risk score for the patient based on the health information using a trained predictor model, determining a second risk score for the patient based on the health information and at least one artificially closed care gap included in the health information using the predictor model, determining a predicted risk reduction score based on the first risk score and the second risk score, determining a patient classification based on the predicted risk reduction score, and outputting a report based on at least one of the first risk score, the second risk score, or the predicted risk reduction score.

Systems and methods for machine learning approaches to management of healthcare populations

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

Growing community of inventors

Danville, PA, United States of America

Linyuan Jing