Abstract: Devices, systems and methods for monitoring glycemic levels in a patient, by calculate a plurality of blood glucose values from a plurality of blood samples taken, calculating a quantile of the blood glucose values taken over a period of days; and creating a Lorenz plot for the blood glucose values in the quantile.

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.

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: Computer apparatus, non-transitory computer readable media and methods for receiving daily vectors of measured blood glucose level values ordered by daily-defined time points over multiple days distributed over a predefined time period; calculating a vector average; calculating a chronic glycemia value based on the vector average modified by a universal term based on the centroid of an attractor basin; receiving subsequent daily vectors of blood glucose level values measured at subsequent defined time points after the predefined time period; calculating an updated vector average; calculating a subsequent chronic glycemia value based on the updated vector average modified by the universal term based on the centroid of the attractor basin estimated from the orbit; and outputting the chronic glycemia value and subsequent chronic glycemia value.

Abstract: Devices, systems and methods for monitoring glycemic levels in a patient, by calculate a plurality of blood glucose values from a plurality of blood samples taken, calculating a quantile of the blood glucose values taken over a period of days; and creating a Lorenz plot for the blood glucose values in the quantile.

Abstract: Computer apparatus, non-transitory computer readable media and methods for receiving daily vectors of measured blood glucose level values ordered by daily-defined time points over multiple days distributed over a predefined time period; calculating a vector average; calculating a chronic glycemia value based on the vector average modified by a universal term based on the centroid of an attractor basin; receiving subsequent daily vectors of blood glucose level values measured at subsequent defined time points after the predefined time period; calculating an updated vector average; calculating a subsequent chronic glycemia value based on the updated vector average modified by the universal term based on the centroid of the attractor basin estimated from the orbit; and outputting the chronic glycemia value and subsequent chronic glycemia value.