David E Probst

US Patent:

20220369961, Nov 24, 2022

Filed:

May 16, 2022

Appl. No.:

17/663657

Inventors:

- Minneapolis 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

International Classification:

A61B 5/145
A61B 5/0205
A61B 5/00
G16H 50/20
G16H 50/30

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.

US Patent:

20200038671, Feb 6, 2020

Filed:

Jul 31, 2018

Appl. No.:

16/050928

Inventors:

- Minneapolis MN, US
Jian Cao - Shoreview MN, US
David Probst - Chandler AZ, US
Daniel Hahn - Tempe AZ, US
Eric C. Maass - Scottsdale AZ, US
Patrick W. Kinzie - Glendale AZ, US

International Classification:

A61N 1/39
A61N 1/04
A61B 5/00
A61B 5/145
A61B 5/0476
A61B 5/021
A61B 5/024
A61B 5/08
A61B 5/11

Abstract:

In some examples, an apparatus configured to be worn by a patient for cardiac defibrillation comprises sensing electrodes configured to sense a cardiac signal of the patient, defibrillation electrodes, therapy delivery circuitry configured to deliver defibrillation therapy to the patient via the defibrillation electrodes, communication circuitry configured to receive data of at least one physiological signal of the patient from at least one sensing device separate from the apparatus, a memory configured to store the data, the cardiac signal, and a machine learning algorithm, and processing circuitry configured to apply the machine learning algorithm to the data and the cardiac signal to probabilistically-determine at least one state of the patient and determine whether to control delivery of the defibrillation therapy based on the at least one probabilistically-determined patient state.