Conference paper
Parameter estimation in type 1 diabetes models for model-based control applications
In this paper, we discuss the identification of a physiological model describing the glucose-insulin dynamics in people with type 1 diabetes (TID). The identified model has to be applied to nonlinear model predictive control (NMPC). We propose a stochastic model of the glucose-insulin dynamics in TID.
Discrete-time glucose data are provided by a continuous glucose monitor (CGM). We use maximum likelihood for parameter estimation, combined with a procedure to compute the gradient of the likelihood function. To test our identification procedure, we generate a virtual population of 10 patients using the Hovorka model and its parameter distribution.
We report the estimates of the model parameters, and we use a validation dataset to evaluate the prediction errors for different prediction intervals. Whereas short-term predictions of blood glucose concentrations are consistent among patients, the accuracy of long-term predictions is more subject to inter-patient variability.
The results suggest that this method has the potential to be used in NMPC algorithms.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2019 |
| Pages: | 4112-4117 |
| Proceedings: | 2019 American Control Conference |
| Series: | Proceedings of the American Control Conference |
| ISBN: | 1538679019 , 1538679264 , 9781538679012 , 9781538679265 , 1538679272 , 1538679280 , 9781538679272 and 9781538679289 |
| ISSN: | 23785861 and 07431619 |
| Types: | Conference paper |
| ORCIDs: | Boiroux, Dimitri , Mahmoudi, Zeinab and Jorgensen, John Bagterp |
Hovorka model NMPC algorithms TID blood blood glucose concentrations discrete-time glucose data diseases glucose monitor glucose-insulin dynamics identification procedure likelihood function maximum likelihood medical control systems model parameters model-based control applications nonlinear control systems nonlinear model predictive control parameter distribution parameter estimation patient monitoring patient treatment physiological model physiological models prediction errors prediction intervals predictive control stochastic model sugar type 1 diabetes models
