Computer-Implemented System And Method For Facilitating Accurate Glycemic Control By Modeling Blood Glucose Using Cloud-Based Circadian Profiles

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. 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, and modeling a change in the dose of the anti-hyperglycemic medication based on glucose lowering effects of the modeled change.

What is claimed is: 
     
         1 . A computer-implemented method for facilitating accurate glycemic control by modeling blood glucose using cloud-based circadian profiles, comprising the steps of:
 defining a plurality of meal periods that each occurs each day at a set time;   defining a plurality of classes that each class comprises a set of anti-hyperglycemic medications that has similar glucose lowering effects;   building a circadian profile for a diabetic patient, comprising the steps of:
 choosing an observational time frame for the circadian profile comprising a plurality of days that have occurred recently; and 
 storing online at least two sets of pre- and post-meal period data that comprise blood glucose levels, doses of anti-hyperglycemic medications that were respectively taken during each of the meal periods for which the blood glucose levels were recorded in a cloud computing infrastructure, and the classes to which the anti-hyperglycemic medications are comprised; and 
   creating a model of glucose management through the circadian profile for the diabetic patient for each of the anti-hyperglycemic medications in the class over the cloud computing infrastructure, comprising:
 validating access to the circadian profile; 
 defining a modeling period comprising a plurality of days, which each comprises the same plurality of the meal periods that occurred each day in the observational time frame; 
 estimating expected blood glucose values and their predicted errors at each of the meal periods from the blood glucose levels in each data in the validated circadian profile that respectively occur at the same set times; 
 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 class, modeling a change in the dose of the anti-hyperglycemic medication for the selected meal period, comprising the steps of:
 obtaining glucose lowering effect of the modeled change in the dose of the anti-hyperglycemic medication and glucose lowering effects of other anti-hyperglycemic medications in the class; 
 normalizing the glucose lowering effect of the modeled change in the dose of the anti-hyperglycemic medication with the glucose lowering effects of the other anti-hyperglycemic medications in the class; 
 propagating the normalized blood glucose lowering effect over time for the modeled change in the dose of the anti-hyperglycemic medication to the expected blood glucose values, beginning with the selected meal period and continuing with each of the meal periods occurring subsequently in the modeling period, the normalized blood glucose lowering effect being adjusted in proportion to the set time of each subsequent meal period until the normalized blood glucose lowering effect is exhausted; and 
 visualizing the expected blood glucose values as propagated and their predicted errors in the log-normal distribution, 
 
   wherein the steps are performed on a suitably-programmed computer.   
     
     
         2 . A method according to  claim 1 , further comprising the step of:
 determining target ranges for blood glucose at each of the meal periods occurring each day in the modeling period and superimposing the target ranges over the expected blood glucose values for each meal period in the log-normal distribution.   
     
     
         3 . A method according to  claim 1 , further comprising the steps of:
 associating a flag with at least one of the blood glucose levels as an atypical measurement; and   processing the flagged blood glucose level in the circadian profile comprising at least one of:
 treating the flagged blood glucose level differently from other blood glucose levels; and 
 discarding the flagged blood glucose level from the circadian profile. 
   
     
     
         4 . A method according to  claim 3 , further comprising the steps of:
 defining the atypical measurement based on at least one of a level of carbohydrate intake, physical activity, and stress of the diabetic patient;   determining a weighting criteria for the flagged blood glucose level; and   assigning a weight to the flagged blood glucose level.   
     
     
         5 . A method according to  claim 1 , further comprising the step of:
 determining the classes of the anti-hyperglycemic medications based on the glucose lowering effect comprising at least one of short-acting, intermediate-acting, and long-acting.   
     
     
         6 . A method according to  claim 5 , further comprising the step of:
 identifying the glucose lowering effect by at least one of hours, days, weeks, months, and years.   
     
     
         7 . A method according to  claim 1 , further comprising the step of:
 modeling a kind of anti-hyperglycemic medication comprising at least one of oral anti-hyperglycemic medication and injectable anti-hyperglycemic medication.   
     
     
         8 . A method according to  claim 1 , further comprising the step of:
 defining the meal periods as comprising, within each day in the observational time frame, breakfast, lunch, dinner, and bedtime meal periods.   
     
     
         9 . A method according to  claim 1 , further comprising the step of:
 integrating further information into the meal period data in the circadian profile comprising at least one of body weight of the diabetic patient, times of measurement of the blood glucose levels, site of injection of the anti-hyperglycemic medication, and comments on lifestyle.   
     
     
         10 . A method according to  claim 1 , further comprising the step of:
 providing the modeled change in the dose of the anti-hyperglycemic medication as dose suggestion comprising at least one of relative dose suggestion and absolute dose suggestion.   
     
     
         11 . A method according to  claim 1 , further comprising at least one of the steps of:
 presenting a qualitative scale for the dose suggestion comprising scales of slight change, moderate change, and significant change; and   presenting a quantitative scale of the dose suggestion comprising a quantum of the anti-hyperglycemic medication.   
     
     
         12 . A method according to  claim 1 , further comprising the steps of:
 including a body weight of the diabetic patient in the circadian profile; and   performing a trend analysis of the body weight over any preceding observational time frames for the selection of the change in the dose of the anti-hyperglycemic medication.   
     
     
         13 . A computer-implemented system for facilitating accurate glycemic control by modeling blood glucose using cloud-based circadian profiles, comprising:
 an electronically-stored database maintained in a cloud computing infrastructure and comprising a plurality of records, each record comprising a circadian profile, comprising:
 a plurality of meal periods that each occurs each day at a set time and divide each circadian profile into the meal periods; 
 a plurality of classes that each class comprises a set of anti-hyperglycemic medications that has similar glucose lowering effects; 
 an observational time frame comprising a plurality of days that have occurred recently; 
 at least two of typical measurements of pre-meal and post-meal self-measured blood glucose that were recorded at each of the meal periods that occurred each day in the observational time frame; 
 a dose of anti-hyperglycemic medication that was taken during each of the meal periods for which the blood glucose measurements were recorded; and 
 one of the classes to which the dose of the anti-hyperglycemic medication is comprised; 
   a user interface for creating a model of glucose management through the circadian profile for the diabetic patient for each of the anti-hyperglycemic medications in the class, comprising:
 an executable application configured to model glucose management, comprising:
 a validation module configured to validate access to the circadian profiles through the cloud computing environment; 
 a model period module configured to define a modeling period comprising a plurality of days, which each comprises the same plurality of the meal periods that occurred each day in the observational time frame; 
 a collection module configured to collect the blood glucose measurements, upon validation, comprising each of the meal periods; 
 a statistical engine configured to determine expected blood glucose values and their predicted errors at each of the meal periods from the blood glucose measurements based on the meal periods in the circadian profile; 
 a log-normal distribution module configured to visualize the expected blood glucose values and their predicted errors over time for each meal period in a log-normal distribution; and 
 a change modeling module configured to select one of the meal periods and, for each anti-hyperglycemic medication in the class, to model a change in the dose of the anti-hyperglycemic medication for the selected meal period, comprising:
 an effect module configured to obtain glucose lowering effect of the modeled change in the dose of the anti-hyperglycemic medication and glucose lowering effects of other anti-hyperglycemic medications in the class; 
 a normalization module configured to normalize the glucose lowering effect of the modeled change in the dose of the anti-hyperglycemic medication with the glucose lowering effects of the other anti-hyperglycemic medications in the class; 
 a propagation module configured to propagate the normalized glucose lowering effect over time for the modeled change in the dose of the anti-hyperglycemic medication to the expected blood glucose values, beginning with the selected meal period and continuing with each of the meal periods occurring subsequently in the modeling period, the normalized blood glucose lowering effect being adjusted in proportion to the set time of each subsequent meal period until the normalized blood glucose lowering effect is exhausted; and 
 a visualization module configured to visualize the expected blood glucose values as propagated and their predicted errors in the log-normal distribution; and 
 
 
 a control panel for operating the change in the dose of the anti-hyperglycemic medication; and 
   a display for displaying the model of glucose management through the circadian profile.   
     
     
         14 . A system according to  claim 13 , further comprising:
 a flag module configured to associate a flag with at least one of the recorded blood glucose measurements as an atypical measurement; and   a flag process module configured to process the flagged blood glucose measurement in the circadian profile comprising at least one of:
 a flag weight module configured to treat the flagged blood glucose measurement differently from other recorded blood glucose measurements; and 
 a deletion module configured to discard the flagged blood glucose measurement from the circadian profile. 
   
     
     
         15 . A system according to  claim 14 , further comprising:
 a classification module configured to define the atypical measurement based on at least one of a level of carbohydrate intake, physical activity, and stress of the diabetic patient;   a criteria module configured to determine a weighting criteria for the flagged blood glucose measurement; and   a weight assignment module configured to assign a weight to the flagged blood glucose measurement.   
     
     
         16 . A system according to  claim 15 , further comprising:
 a threshold of at least one of hypoglycemic risk and hyperglycemic occurrence, which are both expressed as blood glucose values stored in the database;   a warning module configured to identify each of the expected blood glucose values exhibiting either a risk of falling below the hypoglycemic risk threshold or rising above the hyperglycemic occurrence threshold; and   a tooltip for indicating a notice associated with the atypical measurement on the display and explaining a reason why the risk can be ignored.   
     
     
         17 . A system according to  claim 13 , further comprising:
 target ranges stored in the database for the expected blood glucose values at each meal period in the model day;   a target module configured to superimpose the target ranges over the visualized expected blood glucose values;   a change control on the control panel for at least one of increasing and decreasing the dose of the anti-hyperglycemic medication for the meal period on the display, the increasing and decreasing of the anti-hyperglycemic medication being operated by clicking the change control on the control panel;   an adjustment module configured to adjust the expected blood glucose values, comprising at least one of:
 a dose increase module configured to increase the dose of the anti-hyperglycemic medication until the expected blood glucose value for the meal period moves into the target range based on the normalized glucose lowering effect of the change in the dose of the anti-hyperglycemic medication; and 
 a dose decrease module configured to decrease the dose of the anti-hyperglycemic medication until the expected blood glucose value for the meal period moves into the target range based on the normalized glucose lowering effect of the change in the dose of the anti-hyperglycemic medication; and 
   a dose suggestion module configured to determine the changes in the dose of the anti-hyperglycemic medication as a dose suggestion.   
     
     
         18 . A system according to  claim 17 , further comprising:
 a quantum module configured to define a quantum and unit of the anti-hyperglycemic medication from the normalized glucose lowering effect of the anti-hyperglycemic medication applied to the expected blood glucose value as the dose suggestion, comprising:
 a count module configured to count a number of the clicks of the change control on the control panel for the adjustment of the expected blood glucose value into the target range; and 
 a conversion module configured to covert the number of the clicks into a quantum of the anti-hyperglycemic medication. 
   
     
     
         19 . A system according to  claim 13 , further comprising:
 a medication class module configured to determine the classes of the anti-hyperglycemic medications based on the glucose lowering effect comprising at least one of short-acting, intermediate-acting, and long-acting.   
     
     
         20 . A system according to  claim 19 , further comprising:
 a temporality module configured to identify the glucose lowering effect by at least one of hours, days, weeks, months, and years.   
     
     
         21 . A system according to  claim 13 , further comprising:
 a medication kind module configured to model a kind of anti-hyperglycemic medication comprising at least one of oral anti-hyperglycemic medication and injectable anti-hyperglycemic medication.   
     
     
         22 . A system according to  claim 13 , further comprising:
 a meal period module configured to define the meal periods as comprising, within each day in the observational time frame, breakfast, lunch, dinner, and bedtime meal periods.   
     
     
         23 . A system according to  claim 13 , further comprising:
 an integration module configured to integrate further information into the meal period data in the circadian profile comprising at least one of body weight of the diabetic patient, times of measurement of the blood glucose measurements, site of injection of the anti-hyperglycemic medication, and comments on lifestyle.   
     
     
         24 . A system according to  claim 13 , further comprising:
 a suggestion module configured to provide the modeled change in the dose of the anti-hyperglycemic medication as dose suggestion comprising at least one of relative dose suggestion and absolute dose suggestion.   
     
     
         25 . A system according to  claim 24 , further comprising at least one of:
 a qualitative scale for the dose suggestion comprising scales of slight change, moderate change, and significant change; and   a quantitative scale for the dose suggestion comprising a quantum of the anti-hyperglycemic medication.