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.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: A system for creating and updating one or more models for providing treatment recommendations for health conditions may receive weight measurements from a weight measuring device, execute a basic model based on information regarding the health condition, to generate treatment recommendations, generate reminders prompting the user to obtain weight measurements, estimate an impact of the user data on the weight measurements, generate a modified model, based on the basic model, the received weight measurements, the received user data, and the estimated impact of the user data, the modified model including personalized treatment recommendations specific to the user, and transmit the reminders, the treatment recommendations, and the personalized treatment recommendations to the user device.

Abstract: This present disclosure relates to systems for providing an adaptive analytical behavioral health assistant. These systems may obtain glucose measurements from a user, execute a model of a physiological system generic to any user to generate notifications for the user, generate a modified model specific to a physiological system of the user based on the received glucose measurements, execute the modified model to generate personalized notifications, based on the modified model, generate an updated modified model, at least once, based on the modified model and additional glucose measurements, and output the notifications.

Abstract: An adaptive analytical behavioral health assistant may obtain blood pressure measurements from a user, execute a model of a physiological system generic to any user to generate notifications for the user, generate a modified model specific to a physiological system of the user based on the received blood pressure measurements, execute the modified model to generate personalized notifications, generate an updated modified model, at least once, based on the modified model and additional blood pressure measurements, and output the notifications including adjusting a timing of obtaining the blood pressure measurements, the personalized notifications, and the updated personalized notifications to the user.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: An adaptive analytical behavioral health assistant may obtain blood pressure measurements from a user, execute a model of a physiological system generic to any user to generate notifications for the user, generate a modified model specific to a physiological system of the user based on the received blood pressure measurements, execute the modified model to generate personalized notifications, generate an updated modified model, at least once, based on the modified model and additional blood pressure measurements, and output the notifications including adjusting a timing of obtaining the blood pressure measurements, the personalized notifications, and the updated personalized notifications to the user.

Abstract: A system for creating and updating one or more models for providing treatment recommendations for health conditions may receive weight measurements from a weight measuring device, execute a basic model based on information regarding the health condition, to generate treatment recommendations, generate reminders prompting the user to obtain weight measurements, estimate an impact of the user data on the weight measurements, generate a modified model, based on the basic model, the received weight measurements, the received user data, and the estimated impact of the user data, the modified model including personalized treatment recommendations specific to the user, and transmit the reminders, the treatment recommendations, and the personalized treatment recommendations to the user device.

Abstract: An adaptive analytical behavioral health assistant may obtain glucose measurements from a user, execute a model of a physiological system generic to any user to generate notifications for the user, generate a modified model specific to a physiological system of the user based on the received glucose measurements, execute the modified model to generate personalized notifications, based on the modified model, generate an updated modified model, at least once, based on the modified model and additional glucose measurements, and output the notifications.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.