US 12,430,027 B2
Predicting glucose trends for population management
Hugh H. Ryan, Lee's Summit, MO (US); Megan Kathleen Quick, Kansas City, MO (US); and Daniel Craig Crough, Overland Park, KS (US)
Assigned to Cerner Innovation, Inc., Kansas City, MO (US)
Filed by CERNER INNOVATION, INC., Kansas City, KS (US)
Filed on Dec. 10, 2020, as Appl. No. 17/118,021.
Application 17/118,021 is a continuation of application No. 16/130,793, filed on Sep. 13, 2018, granted, now 10,891,053.
Application 16/130,793 is a continuation of application No. 14/581,052, filed on Dec. 23, 2014, granted, now 10,120,979, issued on Nov. 6, 2018.
Prior Publication US 2021/0124487 A1, Apr. 29, 2021
Int. Cl. G06F 3/06 (2006.01); G16H 50/20 (2018.01); G16Z 99/00 (2019.01)
CPC G06F 3/061 (2013.01) [G06F 3/0635 (2013.01); G06F 3/0659 (2013.01); G06F 3/0685 (2013.01); G16H 50/20 (2018.01); G16Z 99/00 (2019.02)] 20 Claims
OG exemplary drawing
 
1. One or more computer storage media storing computer-useable instructions, the instructions when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
establishing a communication channel between at the least one computing device and an integrated home device, wherein the integrated home device administers medication at a current frequency and dosage;
collecting data from the integrated home device representative of a blood glucose level corresponding to a first patient;
generating a real-time prediction value using a predictive model to analyze the data collected from the integrated home device, the predictive model trained using logistic regression analysis of a training data set comprising a plurality of data elements that are relevant to forecasting blood glucose levels, the plurality of data elements including blood glucose levels associated with a plurality of patients, wherein the data collected from the integrated home device and the training data set include blood glucose values and values for each of the plurality of data elements identified by the logistic regression analysis as relevant to forecasting blood glucose levels;
responsive to the real-time prediction value exceeding a predetermined threshold, determining a change to at least one of the current frequency and dosage of medication dispensed by the integrated home device based on the real-time prediction value; and
automatically adjusting a medication level at least by automatically pushing, via the communication channel, the determined change to the at least one of the current frequency and dosage of medication to the integrated home device.