Abstract: A method may receive historical metabolic values for an individual having a first medical condition. A method may provide a first subset of the historical metabolic values to a machine learning model to train a generative machine learning model. A method may generate a first predicted metabolic value based on the first subset of historical metabolic values. A method may calculate a root mean square error (RMSE) between the first predicted metabolic value and a corresponding actual metabolic value of a second subset of historical metabolic values. A method may train the generative machine learning model to minimize the RMSE. A method may generate a trained generative machine learning model based on the training.

Abstract: Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a 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, and determining, using 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.

Abstract: A method may receive historical metabolic values for an individual having a first medical condition. A method may provide a first subset of the historical metabolic values to a machine learning model to train a generative machine learning model. A method may generate a first predicted metabolic value based on the first subset of historical metabolic values. A method may calculate a root mean square error (RMSE) between the first predicted metabolic value and a corresponding actual metabolic value of a second subset of historical metabolic values. A method may train the generative machine learning model to minimize the RMSE. A method may generate a trained generative machine learning model based on the training.

Abstract: Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a 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, and determining, using 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.

Abstract: Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a 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, and determining, using 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.