Detection of changes in patient health based on glucose data

US12426811B2 · kind B2 · utility

Assignee

• Medtronic, Inc. · US

Inventors

• Kamal Deep Mothilal · Maple Grove, US
• Michael D. Eggen · Chisago City, US
• Ning Yu · Columbia Heights, US
• John P. Keane · Marietta, US
• Shantanu Sarkar · San Jose, US
• Randal C. Schulhauser · Phoenix, US
• David Probst · Chandler, US
• Mark R. Boone · Gilbert, US
• Kenneth A Timmerman · Robbinsdale, US
• Stanley J Taraszewski · Plymouth, US
• Matthew A Joyce · Maple Grove, US
• Amruta Paritosh Dixit · Maple Grove, US
• Kathryn Hilpisch · Cottage Grove, US
• Kathryn Ann Milbrandt · Ham Lake, US
• Laura M Zimmerman · Maple Grove, US
• Matthew L. Plante · Stacy, US

Key dates

• Filing date: May 16, 2022
• Grant date: Sep 30, 2025
• Expiry date: Jan 18, 2044

Classification

• Technology area (CPC G): Physics
• CPC primary: G16H50/30
• WIPO field: Medical technology
• WIPO sector: Instruments

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.