US 11,883,208 B2
Machine learning-based system for estimating glucose values based on blood glucose measurements and contextual activity data
Pratik Agrawal, Porter Ranch, CA (US); Chantal M. McMahon, Atlanta, GA (US); Huzefa F. Neemuchwala, Simi Valley, CA (US); Yuxiang Zhong, Arcadia, CA (US); and John Hoebing, Northridge, CA (US)
Assigned to MEDTRONIC MINIMED, INC., Northridge, CA (US)
Filed by MEDTRONIC MINIMED, INC., Northridge, CA (US)
Filed on Aug. 6, 2019, as Appl. No. 16/533,470.
Prior Publication US 2021/0038163 A1, Feb. 11, 2021
Int. Cl. A61B 5/00 (2006.01); A61B 5/11 (2006.01); A61B 5/145 (2006.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 20/17 (2018.01)
CPC A61B 5/7267 (2013.01) [A61B 5/1118 (2013.01); A61B 5/14532 (2013.01); A61B 5/4839 (2013.01); A61B 5/4866 (2013.01); A61B 5/7221 (2013.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); A61B 5/0002 (2013.01); G16H 20/17 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A processor-implemented method, comprising:
receiving a first set of inputs comprising contextual activity data collected from a source of user activity data, and a second set of inputs comprising intermittent glucose measurements provided from a blood glucose meter; and
processing, via an estimation model, the first set of inputs and the second set of inputs to generate a set of estimated real-time glucose values without using information from a continuous glucose monitor, wherein the generated set of estimated real-time glucose values are not inputted into the estimation model, and the estimation model is an ensemble model that comprises: one or more machine learning models, and a physiological model, wherein the physiological model is configured to be used in conjunction with the one or more machine learning models to confine the generated set of estimated real-time glucose values within an acceptable range.