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.

Abstract: A computer-implemented system and method for facilitating accurate glycemic control by modeling blood glucose using cloud-based circadian profiles is provided. Anti-hyperglycemic medications are categorized based on similar glucose lowering effects. A circadian profile for a diabetic patient is built by storing online at least two recent typical measurements of pre-meal and post-meal blood glucose, a dose of an anti-hyperglycemic medication, and the class of the anti-hyperglycemic medication.

Abstract: A computer-implemented system and method for facilitating accurate glycemic control by modeling blood glucose using circadian profiles is provided. Anti-hyperglycemic medications are categorized based on similar glucose lowering effects. A circadian profile for a diabetic patient is built by assembling meal period data including at least two recent typical measurements of pre-meal and post-meal self-measured blood glucose, identifying a dose of an anti-hyperglycemic medication, and identifying the class of the anti-hyperglycemic medication.

Abstract: A blood glucose meter and computer-implemented method for facilitating accurate glycemic control by modeling blood glucose using circadian profiles is provided. Anti-hyperglycemic medications are categorized based on similar glucose lowering effects. A circadian profile is built by collecting at least two recent typical measurements of pre-meal and post-meal period data stored on a glucometer, identifying a dose of an anti-hyperglycemic medication, and identifying the class of the anti-hyperglycemic medication. A model of glucose management through the circadian profile is created by estimating expected blood glucose values and their predicted errors at each of the meal periods, visualizing the expected blood glucose values and their predicted errors over time for each meal period in a log-normal distribution, and selecting one of the meal periods and, for each anti-hyperglycemic medication in the identified class, modeling a change in the dose of the anti-hyperglycemic medication.

Abstract: A computer-implemented system and method for improving glucose management through cloud-based modeling of circadian profiles is provided. For each daily meal period, 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.

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 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 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 period, 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.