Bayesian framework for personalized model identification and prediction of future blood glucose in type 1 diabetes using easily accessible patient data

A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method of predicting future blood glucose concentrations of an individual patient having a continuous glucose monitoring (CGM) sensor, the method comprising:

• selecting an individualized nonlinear physiological model of glucose-insulin dynamics, the selected individualized nonlinear physiological model having a plurality of model parameters whose values are to be determined;
• estimating population-based values of a first subset of the plurality of model parameters from a priori population data, wherein the estimated population-based values of the first subset relate to at least one of insulin absorption or glucose absorption;
• estimating personalized values of a second subset of the model parameters by applying a parameter estimation technique to a priori information about a probability distribution of each parameter of the second subset and data for the individual patient, the data for the individual patient comprising insulin infusion information for the individual patient and carbohydrate intake information for the individual patient;
• obtaining an a posteriori probability function based on past glucose measurements of the individual patient from the CGM sensor, the estimated population-based values of the first subset of the plurality of model parameters, and the estimated personalized values of the second subset of the plurality of model parameters;
• receiving a current glucose measurement of the individual patient from the CGM sensor;
• updating the posteriori probability function based on the current glucose measurement;
• applying a particle filter to the selected individualized nonlinear physiological model using the updated a posteriori probability function to obtain a predicted blood glucose concentration of the individual patient at a future time;
• generating a notification based on the predicted blood glucose concentration indicating upcoming dysglycemia, the notification comprising a proactive alert of the upcoming dysglycemia;
• and
• causing the notification to be output on a device associated with the individual patient.