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:
Sep. 30, 2025
Filed:
May. 16, 2022
Medtronic, Inc., Minneapolis, MN (US);
Kamal Deep Mothilal, Maple Grove, MN (US);
Michael D. Eggen, Chisago City, MN (US);
Ning Yu, Columbia Heights, MN (US);
John P Keane, Shoreview, MN (US);
Shantanu Sarkar, Roseville, MN (US);
Randal C. Schulhauser, Phoenix, AZ (US);
David L. Probst, Chandler, AZ (US);
Mark R. Boone, Gilbert, AZ (US);
Kenneth A Timmerman, Robbinsdale, MN (US);
Stanley J Taraszewski, Plymouth, MN (US);
Matthew A Joyce, Maple Grove, MN (US);
Amruta Paritosh Dixit, Maple Grove, MN (US);
Kathryn E. Hilpisch, Cottage Grove, MN (US);
Kathryn Ann Milbrandt, Ham Lake, MN (US);
Laura M Zimmerman, Maple Grove, MN (US);
Matthew L Plante, Danbury, WI (US);
Medtronic, Inc., Minneapolis, MN (US);
Abstract
This disclosure is directed to systems and techniques for detecting change in patient health based upon patient data. In one example, a medical system comprising processing circuitry communicably coupled to a glucose sensor and configured to generate continuous glucose sensor measurements of a patient. The processing circuitry is further configured to: extract at least one feature from the continuous glucose sensor measurements over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, a number of hyperglycemia events, or one or more statistical metrics corresponding to the continuous glucose sensor measurements; apply a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event; and generate output data based on the risk of the cardiovascular event.