US 11,657,921 B2
Artificial intelligence based cardiac event predictor systems and methods
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); and Thomas Morland, Danville, PA (US)
Assigned to Tempus Labs, Inc., Chicago, IL (US); and Geisinger Clinic, Danville, PA (US)
Filed by Tempus Labs, Inc., Chicago, IL (US); and Geisinger Clinic, Danville, PA (US)
Filed on May 31, 2022, as Appl. No. 17/829,356.
Application 17/829,356 is a continuation in part of application No. 17/026,092, filed on Sep. 18, 2020.
Claims priority of provisional application 63/194,923, filed on May 28, 2021.
Claims priority of provisional application 63/202,436, filed on Jun. 10, 2021.
Claims priority of provisional application 63/224,850, filed on Jul. 22, 2021.
Claims priority of provisional application 62/902,266, filed on Sep. 18, 2019.
Claims priority of provisional application 62/924,529, filed on Oct. 22, 2019.
Claims priority of provisional application 63/013,897, filed on Apr. 22, 2020.
Prior Publication US 2022/0378379 A1, Dec. 1, 2022
Int. Cl. G16H 50/30 (2018.01); G16H 50/20 (2018.01); A61B 5/318 (2021.01); A61B 5/00 (2006.01); A61B 5/28 (2021.01); G06K 9/62 (2022.01)
CPC G16H 50/30 (2018.01) [A61B 5/0006 (2013.01); A61B 5/28 (2021.01); A61B 5/318 (2021.01); A61B 5/7275 (2013.01); G16H 50/20 (2018.01); G06K 9/6259 (2013.01)] 29 Claims
OG exemplary drawing
 
1. A method for determining cardiac disease risk from electrocardiogram trace data and clinical data, comprising:
receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads and a time interval and comprising, for each lead included in the plurality of leads, voltage data associated with at least a portion of the time interval;
receiving clinical data associated with the patient;
providing the clinical data and at least a portion of the electrocardiogram trace data to a trained machine learning model, the model trained to evaluate the clinical data and the portion of the electrocardiogram trace data with respect to one or more cardiac disease states;
generating, by the trained machine learning model and based on the evaluation, a risk score reflecting a likelihood of the patient being diagnosed with a cardiac disease state within a predetermined period of time from when the electrocardiogram trace data was generated; and
outputting the risk score to at least one of a memory or a display.