# US 12,594,040 B2 · Bayesian framework for personalized model identification and prediction of future blood glucose in type 1 diabetes using easily accessible patient data

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- Number: US 12,594,040 B2
- Document ID: US12594040B2
- Status: Granted
- Kind: B2
- Application number: 17/454,693
- Filed: 2021-11-12
- Published: 2022-07-28
- Granted: 2026-04-07
- Earliest claimed: 2021-01-25
- Assignees: DexCom, Inc.
- Applicants: DexCom, Inc.
- Inventors: Giacomo Cappon; Andrea Facchinetti; Giovanni Sparacino; Simone Del Favero
- Web page: https://patentgrep.com/patent/US12594040B2

## CPC

- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement}
- A61B 5/725: {using specific filters therefor, e.g. Kalman or adaptive filters (specific diagnostics methods using using bioelectric or biomagnetic signals A61B5/316)}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/50: for simulation or modelling of medical disorders
- G16H 20/17: delivered via infusion or injection
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A method of predicting future blood glucose concentrations of an individual patient includes: selecting an individualized nonlinear physiological model of glucose-insulin dynamics, the selected model having a plurality of model parameters whose values are to be determined; estimating values for each of the model parameters in the plurality of model parameters, a first subset of the model parameters having values estimated from a priori population data and a second subset of the model parameters having values personalized for the individual patient by applying a parameter estimation technique to a priori information and data for the individual patient to obtain a posteriori information; and; applying a nonlinear prediction technique to the selected model using the estimated values for each of the model parameters to obtain a predicted blood glucose concentration of the individual patient at a future time.

## Claims

### Claim 1

A method of predicting future blood glucose concentrations of an individual patient having a continuous glucose monitoring (CGM) sensor, comprising:
selecting, by a processor, 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, by the processor, 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, by the processor, 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, by the processor, 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, by the processor, a current glucose measurement of the individual patient from the CGM sensor;
updating, by the processor, the posteriori probability function based on the current glucose measurement;
applying, by the processor, 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, by the processor, a notification based on the predicted blood glucose concentration indicating upcoming dysglycemia, the notification comprising a proactive alert of the upcoming dysglycemia; and
causing, by the processor, the notification to be output on a device associated with the individual patient.

### Claim 2 (depends on 1)

The method of claim 1, wherein the parameter estimation technique is a Markov Chain Monte Carlo (MCMC) Bayesian estimator.

### Claim 3 (depends on 1)

The method of claim 1, wherein the parameter estimation technique is selected from the group consisting of a maximum a posteriori technique, a maximum likelihood technique and a prediction error minimization technique.

### Claim 4 (depends on 1)

The method of claim 1, wherein the selected individualized nonlinear physiological model includes a subcutaneous insulin absorption sub-model, an oral glucose absorption sub-model and a glucose-insulin kinetics model.

### Claim 5 (depends on 4)

The method of claim 4, wherein the subcutaneous insulin absorption sub-model includes a first model parameter specifying a value of insulin distribution and a second model parameter specifying a delay in an appearance of insulin in a first compartment, the first and second model parameters being in the first subset of the plurality of model parameters having the estimated population-based values.

### Claim 6 (depends on 5)

The method of claim 5, wherein the subcutaneous insulin absorption sub-model includes a third model parameter specifying a diffusion rate constant from a first to a second compartment and a fourth model parameter specifying a rate constant of subcutaneous insulin absorption from the second compartment to plasma, the third and fourth model parameters being in the second subset of the plurality of model parameters having the estimated personalized values.

### Claim 7 (depends on 1)

The method of claim 1, wherein the selected individualized nonlinear physiological model is a maximal physiological model in which selected ones of the plurality of model parameters are neglected.

### Claim 8 (depends on 1)

The method of claim 1, further comprising augmenting the selected individualized nonlinear physiological model with a residual error model to describe residual modeling errors.

### Claim 9 (depends on 8)

The method of claim 8, wherein the residual modeling errors are modeled as an Auto regressive Integrated Moving Average (ARIMA) of order (5,5,1).

### Claim 10 (depends on 8)

The method of claim 8, wherein the residual error model has model parameters estimated using a predicted error method (PEM).

### Claim 11 (depends on 1)

The method of claim 1, wherein the predicted blood glucose concentration is predicted at a plurality of different times.

### Claim 12

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.

### Claim 13 (depends on 12)

The computer-program product of claim 12, wherein the parameter estimation technique is a Markov Chain Monte Carlo (MCMC) Bayesian estimator.

### Claim 14 (depends on 12)

The computer-program product of claim 12, wherein the parameter estimation technique is selected from the group consisting of a maximum a posteriori technique, a maximum likelihood technique and a prediction error minimization technique.

### Claim 15 (depends on 12)

The computer-program product of claim 12, wherein the selected individualized nonlinear physiological model includes a subcutaneous insulin absorption sub-model, an oral glucose absorption sub-model and a glucose-insulin kinetics model.

### Claim 16 (depends on 15)

The computer-program product of claim 15, wherein the subcutaneous insulin absorption sub-model includes a first model parameter specifying a value of insulin distribution and a second model parameter specifying a delay in an appearance of insulin in a first compartment, the first and second model parameters being in the first subset of the plurality of model parameters having the estimated population-based values.

### Claim 17 (depends on 16)

The computer-program product of claim 16, wherein the subcutaneous insulin absorption sub-model includes a third model parameter specifying a diffusion rate constant from a first to a second compartment and a fourth model parameter specifying a rate constant of subcutaneous insulin absorption from the second compartment to plasma, the third and fourth model parameters being in the second subset of the plurality of model parameters having the estimated personalized values.

### Claim 18 (depends on 1)

The method of claim 1, wherein the notification comprises a recommended treatment to mitigate the upcoming dysglycemia indicated by the predicted blood glucose concentration.

### Claim 19 (depends on 1)

The method of claim 1, wherein the device associated with the individual patient comprises the processor.

### Claim 20 (depends on 1)

The method of claim 1, wherein the causing comprises communicating, by the processor, the notification to the device associated with the individual patient.