Forecasting blood glucose concentration
A method, a system and a computer program product for forecasting blood glucose concentration. One or more features for training a blood glucose concentration forecasting model are determined. The features are determined based on one or more input data parameters associated with a user in a plurality of users. Using the determined one or more features, the blood glucose concentration forecasting model is trained. Using the trained blood glucose concentration forecasting model, one or more expected blood glucose concentrations for the user are generated.
1 . A computer-implemented method, comprising:
determining one or more features for training a blood glucose concentration forecasting model, wherein the one or more features are determined based on one or more input data parameters associated with a user in a plurality of users;
training, using the determined one or more features, the blood glucose concentration forecasting model; and
generating, using the trained blood glucose concentration forecasting model, one or more expected blood glucose concentrations for the user.
2 . The method according to claim 1 , further comprising displaying the generated one or more expected blood glucose concentrations for the user on one or more graphical user interfaces.
3 . The method according to claim 1 , wherein the training further comprises training the blood glucose concentration forecasting model using one or more parameters associated with one or more another users in the plurality of users.
4 . The method according to claim 3 , wherein the one or more parameters associated with one or more another users including one or more historical data parameters associated with one or more another users.
5 . The method according to claim 1 , wherein the one or more input parameters include at least one of the following: a data indicating a type of diabetes of the user, a data indicating a medical condition of the user, a data indicating medication being consumed by the user, a data indicating a meal consumed by the user, a data indicating a physical activity performed by the user, a data indicating a time of a blood glucose concentration measurement of the user, a data indicating at least one of a previous and a current value of a blood glucose concentration measurement of the user, a data indicating a time of a previous blood glucose concentration forecast, a data indicating a target blood glucose concentration (a1c) for the user, a data indicating at least one of a current date and a current time, a data indicating a weight of the user, a data indicating one or more changes in the blood glucose concentration of the user, a data indicating one or more carbohydrate values as consumed by the user, and any combination thereof.
6 . The method according to claim 1 , wherein the generating further comprises
generating one or more target blood glucose concentration ranges for the user;
generating one or more confidence intervals for the generated one or more expected blood glucose concentrations, the confidence intervals being indicative of an accuracy of the generated one or more expected blood glucose concentrations; and
comparing the generated one or more target blood glucose concentration ranges, the one or more confidence intervals for the generated one or more expected blood glucose concentrations, and the generated one or more expected blood glucose concentrations.
7 . The method according to claim 6 , further comprising displaying, based on the comparison, an indication whether the generated one or more expected blood glucose concentrations is within the one or more target blood glucose concentration ranges.
8 . The method according to claim 7 , further comprising generating an alert to the user when the generated one or more expected blood glucose concentrations is not within the one or more target blood glucose concentration ranges.
9 . The method according to claim 1 , wherein the generated one or more expected blood glucose concentrations is generated for a point in time subsequent to the determining.
10 . The method according to claim 1 , further comprising
repeating the determining and the training;
generating, based on the repeating, an updated one or more expected blood glucose concentrations for the user.
11 . A system comprising:
at least one programmable processor; and
a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
determining one or more features for training a blood glucose concentration forecasting model, wherein the one or more features are determined based on one or more input data parameters associated with a user in a plurality of users;
training, using the determined one or more features, the blood glucose concentration forecasting model; and
generating, using the trained blood glucose concentration forecasting model, one or more expected blood glucose concentrations for the user.
12 . The system according to claim 11 , wherein the operations further comprise displaying the generated one or more expected blood glucose concentrations for the user on one or more graphical user interfaces.
13 . The system according to claim 11 , wherein the training further comprises training the blood glucose concentration forecasting model using one or more parameters associated with one or more another users in the plurality of users.
14 . The system according to claim 13 , wherein the one or more parameters associated with one or more another users including one or more historical data parameters associated with one or more another users.
15 . The system according to claim 11 , wherein the one or more input parameters include at least one of the following: a data indicating a type of diabetes of the user, a data indicating a medical condition of the user, a data indicating medication being consumed by the user, a data indicating a meal consumed by the user, a data indicating a physical activity performed by the user, a data indicating a time of a blood glucose concentration measurement of the user, a data indicating at least one of a previous and a current value of a blood glucose concentration measurement of the user, a data indicating a time of a previous blood glucose concentration forecast, a data indicating a target blood glucose concentration (a1c) for the user, a data indicating at least one of a current date and a current time, a data indicating a weight of the user, a data indicating one or more changes in the blood glucose concentration of the user, a data indicating one or more carbohydrate values as consumed by the user, and any combination thereof.
16 . The system according to claim 11 , wherein the generating further comprises
generating one or more target blood glucose concentration ranges for the user;
generating one or more confidence intervals for the generated one or more expected blood glucose concentrations, the confidence intervals being indicative of an accuracy of the generated one or more expected blood glucose concentrations; and
comparing the generated one or more target blood glucose concentration ranges, the one or more confidence intervals for the generated one or more expected blood glucose concentrations, and the generated one or more expected blood glucose concentrations.
17 . The system according to claim 16 , wherein the operations further comprise displaying, based on the comparison, an indication whether the generated one or more expected blood glucose concentrations is within the one or more target blood glucose concentration ranges.
18 . The system according to claim 17 , wherein the operations further comprise generating an alert to the user when the generated one or more expected blood glucose concentrations is not within the one or more target blood glucose concentration ranges.
19 . The system according to claim 11 , wherein the generated one or more expected blood glucose concentrations is generated for a point in time subsequent to the determining.
20 . The system according to claim 11 , wherein the operations further comprise
repeating the determining and the training;
generating, based on the repeating, an updated one or more expected blood glucose concentrations for the user.
21 . A computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
determining one or more features for training a blood glucose concentration forecasting model, wherein the one or more features are determined based on one or more input data parameters associated with a user in a plurality of users;
training, using the determined one or more features, the blood glucose concentration forecasting model; and
generating, using the trained blood glucose concentration forecasting model, one or more expected blood glucose concentrations for the user.
22 . The computer program product according to claim 21 , wherein the operations further comprise displaying the generated one or more expected blood glucose concentrations for the user on one or more graphical user interfaces.
23 . The computer program product according to claim 21 , wherein the training further comprises training the blood glucose concentration forecasting model using one or more parameters associated with one or more another users in the plurality of users.
24 . The computer program product according to claim 23 , wherein the one or more parameters associated with one or more another users including one or more historical data parameters associated with one or more another users.
25 . The computer program product according to claim 21 , wherein the one or more input parameters include at least one of the following: a data indicating a type of diabetes of the user, a data indicating a medical condition of the user, a data indicating medication being consumed by the user, a data indicating a meal consumed by the user, a data indicating a physical activity performed by the user, a data indicating a time of a blood glucose concentration measurement of the user, a data indicating at least one of a previous and a current value of a blood glucose concentration measurement of the user, a data indicating a time of a previous blood glucose concentration forecast, a data indicating a target blood glucose concentration (a1c) for the user, a data indicating at least one of a current date and a current time, a data indicating a weight of the user, a data indicating one or more changes in the blood glucose concentration of the user, a data indicating one or more carbohydrate values as consumed by the user, and any combination thereof.
26 . The computer program product according to claim 21 , wherein the generating further comprises
generating one or more target blood glucose concentration ranges for the user;
generating one or more confidence intervals for the generated one or more expected blood glucose concentrations, the confidence intervals being indicative of an accuracy of the generated one or more expected blood glucose concentrations; and
comparing the generated one or more target blood glucose concentration ranges, the one or more confidence intervals for the generated one or more expected blood glucose concentrations, and the generated one or more expected blood glucose concentrations.
27 . The computer program product according to claim 26 , wherein the operations further comprise displaying, based on the comparison, an indication whether the generated one or more expected blood glucose concentrations is within the one or more target blood glucose concentration ranges.
28 . The computer program product according to claim 27 , wherein the operations further comprise generating an alert to the user when the generated one or more expected blood glucose concentrations is not within the one or more target blood glucose concentration ranges.
29 . The computer program product according to claim 21 , wherein the generated one or more expected blood glucose concentrations is generated for a point in time subsequent to the determining.
30 . The computer program product according to claim 21 , wherein the operations further comprise
repeating the determining and the training;
generating, based on the repeating, an updated one or more expected blood glucose concentrations for the user.