The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 04, 2026
Filed:
Apr. 12, 2023
Tempus Ai, Inc., Chicago, IL (US);
Geisinger Clinic, Danville, PA (US);
Noah Zimmerman, Redwood City, CA (US);
Brandon Fornwalt, Chicago, IL (US);
John Pfeifer, Chicago, IL (US);
Ruijun Chen, Chicago, IL (US);
Arun Nemani, Chicago, IL (US);
Greg Lee, Chicago, IL (US);
Steve Steinhubl, Chicago, IL (US);
Christopher Haggerty, Danville, PA (US);
Sushravya Raghunath, Danville, PA (US);
Alvaro Ulloa-Cerna, Danville, PA (US);
Linyuan Jing, Danville, PA (US);
Thomas Morland, Danville, PA (US);
Tempus AI, Inc., Chicago, IL (US);
Geisinger Clinic, Danville, PA (US);
Abstract
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.