@inproceedings{4646cd190abf43fb83412e61bdd11079,
title = "Parameter estimation in type 1 diabetes models for model-based control applications",
abstract = "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.",
author = "Dimitri Boiroux and Zeinab Mahmoudi and Jorgensen, {John Bagterp}",
year = "2019",
month = jul,
day = "1",
language = "English",
series = "Proceedings of the American Control Conference",
publisher = "IEEE",
pages = "4112--4117",
booktitle = "Proceedings of 2019 American Control Conference",
address = "United States",
note = "2019 American Control Conference, ACC 2019 ; Conference date: 10-07-2019 Through 12-07-2019",
}