Abstract: A computer-implemented system and method for improving glucose management through modeling of circadian profiles is provided. Meal period data is assembled, which includes, for each stated period, at least two measurements of pre-meal and post-meal self-measured blood glucose. Diabetes medication dosed over the periods is identified. The assembled meal period data and the medication are organized into a circadian profile for a diabetic patient. A model including expected blood glucose values and their predicted errors at each period is created from the blood glucose measurements and is visualized in a log-normal distribution. Target ranges for blood glucose at each period are determined and superimposed over the expected blood glucose values. Pharmacodynamics of the medication are obtained. An incremental change in dosing of the medication is propagated over a model day and the expected blood glucose values and their predicted errors are adjusted in response to the incremental dosing change.

Abstract: A blood glucose meter and computer-implemented method for improving glucose management through modeling of circadian profiles is provided. For each daily meal period, two sets of pre- and post-meal period data are collected into a circadian profile and stored on a glucose meter, including a level of blood glucose of a diabetic patient and a dosage of diabetes medication. A model of predicted blood glucose for the patient is created from the blood glucose levels in each record as expected blood glucose values and predicted errors and visualized in a log-normal distribution. Target ranges for blood glucose at each meal period are determined and superimposed over the expected blood glucose values. Pharmacodynamics of the medication are obtained. An incremental change in dosing of the medication is propagated over a model day and the expected blood glucose values and their predicted errors are adjusted in response to the incremental dosing change.

Abstract: A computer-implemented system and method for improving glucose management through cloud-based modeling of circadian profiles is provided. For each daily meal peeriod, two sets of pre- and post-meal period data that include a blood glucose level and a diabetes medication dosing are stored into a circadian profile for a diabetic patient in a cloud computing infrastructure. Predicted blood glucose is modeled over the infrastructure and the access will be validated. A model, including expected blood glucose values and their predicted errors is created from the blood glucose levels in each profile and visualized in a log-normal distribution. Target ranges for blood glucose are determined and superimposed over the expected values. Pharmacodynamics of the medication are obtained. An incremental change in dosing of the medication is propagated over a model day and the expected blood glucose values and their predicted errors are adjusted in response to the incremental dosing change.