| CPC G16H 50/30 (2018.01) [G16H 10/60 (2018.01)] | 17 Claims |
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1. A computer-implemented method for predicting health and engagement levels for a user, the method comprising:
sensing a user's glucose levels using a continuous glucose monitoring (CGM) device over a time period;
receiving the user's glucose levels collected by the CGM device;
receiving engagement data associated with the user, the engagement data collected by a computing device over the time period, wherein the engagement data is associated with the user's medication activity, diet activity, physical activity, laboratory results, education activity, and CGM device usage, wherein at least some of the engagement data is collected using one or more sensors associated with the user, the one or more sensors including at least one of a weight scale, a blood pressure monitor, an activity tracker, a heart rate monitor, a multi-purpose wearable device, the CGM device, and a ketone tracking device;
determining a first glycemia risk index (GRI) value based on a first amount of time the user is hypoglycemic during the time period and a second amount of time the user is hyperglycemic during the time period;
determining a time in range (TIR) value of the user's glucose level, wherein the determined TIR value is based on an amount of time the user's glucose level is within a threshold band over the time period, wherein the threshold band is determined based on lifestyle, habits, and medical test results of the user;
inputting the user's glucose levels and the engagement data into a machine learning model;
outputting, by the machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, wherein the one or more predictions for future glucose levels for the user further comprise a prediction that a future TIR value is one of within a threshold value of the determined TIR value, greater by more than threshold value, or less by more than the threshold value, wherein the one or more predictions for future glucose levels for the user further comprise a prediction that a future GRI value is in a higher GRI zone than the first GRI zone or in a lower GRI zone than the first GRI zone, wherein the one or more predictions for future glucose levels for the user further comprise a prediction that the future GRI value is in the first GRI zone, is in a second GRI zone higher than the first GRI zone, or is in a third GRI zone lower than the first GRI zone, wherein the one or more predictions for future glucose levels for the user further comprise a prediction that the future GRI value is greater than a threshold GRI value or less than the threshold GRI value;
outputting, by the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels, wherein the one or more predictions for future engagement levels comprises a prediction that a future CGM device engagement level is above a threshold amount of CGM device engagement or below the threshold amount of CGM device engagement, wherein CGM device engagement includes a measure of CGM device use by the user, wherein the one or more predictions for future engagement levels further comprise a prediction that a future engagement level is a high engagement state or a low engagement state, and wherein the one or more predictions for future engagement levels further comprise a prediction that a future manual engagement level is a first manual engagement state or a second manual engagement state;
calculating, by the machine learning model, a timing and a dosing amount of basal insulin for the user at regular intervals;
administering, via an insulin pump, in response to an output of the machine learning model, at the timing of basal insulin at the regular intervals, the dosing amount of basal insulin to the user;
calculating, by the machine learning model, a timing and a dosing amount of bolus insulin for the user, the timing corresponding to mealtimes;
administering, via the insulin pump, in response to an output of the machine learning model, at the timing of bolus insulin at the mealtimes, the dosing amount of bolus insulin to the user;
providing, by the machine learning model, notifications to the user in response to calculating the timing and the dosing amount of basal insulin and the timing and the dosing amount of bolus insulin, wherein the notifications are provided based on the one or more predictions of future engagement levels; and
synchronizing administration, via the insulin pump, in response to an output of the machine learning model, the dosing amount of basal insulin and the dosing amount of bolus insulin to the user.
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