Method to determine individualized insulin sensitivity and optimal insulin dose by linear regression, and related systems

Abstract

This invention relates to a method and a device for predicting the glucose concentration of a subject and recommending therapeutic action. The responses of the user's glucose to administered doses of insulin, dietary carbohydrates, and other factors influencing glucose concentration are measured individually for a given user. Once these responses are learned as a function of time, the method and device can receive information about the factors that have been recently or will soon be administered and can recommend which other factors should also be administered.

1 - 7 . (canceled) 
     
     
         8 . A diabetes management system for recommending a size of a user insulin dose, the system comprising:
 a glucose monitor configured to provide user glucose readings;   an insulin delivery mechanism;   a memory module, the memory module configured to receive and store a record of insulin doses previously administered to a user as a function of time, the memory module configured to receive and store a confidence interval of a response of a user's glucose concentration to the insulin doses; and   a processor module configured to use a mathematical model to calculate an optimal dose of insulin to be taken by the user, the mathematical model comprising:
 projecting a time series of future glucose concentrations over a predetermined amount of time; and 
 minimizing an error function that penalizes deviations away from a predetermined ideal glucose level, 
 wherein the error function penalizes deviations of the glucose concentration below the predetermined ideal glucose level more than deviations of the glucose concentration above the predetermined ideal glucose level; and 
   a display module to display the optimal dose of insulin.   
     
     
         9 . The system of  claim 8 , wherein the mathematical model is a linear regression algorithm where a dependent variable is change in glucose over possibly overlapping time intervals and independent variables are doses of insulin, dietary carbohydrates, and other factors that may affect glucose summed over windows of time prior to the time window during which the glucose change is being regressed. 
     
     
         10 . The system of  claim 9 , wherein the other factors that may affect glucose include a duration of exercise engaged in by a user. 
     
     
         11 . The system of  claim 8 , wherein the mathematical model is a linear regression algorithm where a dependent variable is change in glucose over possibly overlapping time intervals and independent variables are doses of insulin, dietary carbohydrates, and other factors that may affect glucose multiplied by a polynomial dependent on the time delay between when the dose was administered and when the glucose response is being measured. 
     
     
         12 . The system of  claim 11 , wherein the other factors that may affect glucose include duration of exercise engaged in by the user. 
     
     
         13 . The system of  claim 8 , wherein the insulin delivery mechanism comprising a delivery mechanism for delivering short-acting insulin. 
     
     
         14 . The system of  claim 13 , further comprising a second insulin delivery mechanism comprising a delivery mechanism for delivering long-acting insulin. 
     
     
         15 . The system of  claim 14 , wherein the system is configured to suggest an ideal dose of the long-acting insulin and an ideal time to dose the long-acting insulin. 
     
     
         16 . The system of  claim 13 , wherein the system suggests an ideal correction dose of the short-acting insulin for different levels of hyperglycemia. 
     
     
         17 . A diabetes management system for recommending the size of a user insulin dose, the system comprising:
 a glucose monitor configured to provide a user glucose readings;   an insulin delivery mechanism;   a memory module, the memory module configured to receive and store a record of insulin doses previously administered to the user as a function of time; and   a processor module configured to: (i) derive rates of change in a user's glucose concentration as a function of time from a plurality of glucose concentrations stored in the memory module, (ii) derive a glucose response as a function of time that is a fit to a plurality of glucose concentrations and insulin doses, the fit being based on a mathematical model, (iii) integrate the glucose response as a function of time to obtain a user's total glucose response to insulin doses, and (iv) calculate an optimal dose of insulin to be taken by the user by projecting a time series of future glucose concentrations over a predetermined amount of time and minimizing an error function that penalizes deviations away from a predetermined ideal glucose level; and   a display module to display the optimal dose of insulin.   
     
     
         18 . The system of  claim 17 , wherein the mathematical model is a linear regression algorithm where a dependent variable is change in glucose over possibly overlapping time intervals and independent variables are doses of insulin, dietary carbohydrates, and other factors that may affect glucose summed over windows of time prior to the time window during which the glucose change is being regressed. 
     
     
         19 . The system of  claim 18 , wherein the other factors that may affect glucose include a duration of exercise engaged in by the user. 
     
     
         20 . The system of  claim 17 , wherein the mathematical model is a linear regression algorithm where a dependent variable is change in glucose over possibly overlapping time intervals and independent variables are doses of insulin, dietary carbohydrates, and other factors that may affect glucose multiplied by a polynomial dependent on the time delay between when the dose was administered and when the glucose response is being measured. 
     
     
         21 . The system of  claim 20 , wherein the other factors that may affect glucose include duration of exercise engaged in by the user. 
     
     
         22 . The system of  claim 17 , wherein the error function penalizes deviations of the glucose concentration below the predetermined ideal glucose level more than deviations of the glucose concentration above the predetermined ideal glucose level, whereby a glucose concentration below the ideal level is made less likely. 
     
     
         23 . The system of  claim 17 , wherein the insulin delivery mechanism is a delivery mechanism for delivering short-acting insulin. 
     
     
         24 . The system of  claim 23 , further comprising a second insulin delivery mechanism for delivering long-acting insulin. 
     
     
         25 . The system of  claim 24 , wherein the system is configured to suggest an ideal dose of the long-acting insulin and an ideal time to dose the long-acting insulin. 
     
     
         26 . The system of  claim 23 , wherein the system suggests an ideal correction dose of the short-acting insulin for different levels of hyperglycemia.