Machine learning-based system for estimating glucose values based on blood glucose measurements and contextual activity data

A computing device comprising:

• one or more processors;
• and
• one or more processor-readable media storing instructions which, when executed by the one or more processors, cause performance of one or more operations 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.

One or more non-transitory processor-readable media storing instructions which, when executed by one or more processors, cause performance of:

• 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.