Conference paper · Journal article
Model Identification using Continuous Glucose Monitoring Data for Type 1 Diabetes
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
This paper addresses model identification of continuous-discrete nonlinear models for people with type 1 diabetes using sampled data from a continuous glucose monitor (CGM). We compare five identification techniques: least squares, weighted least squares, Huber regression, maximum likelihood with extended Kalman filter and maximum likelihood with unscented Kalman filter.
We perform the identification on a 24-hour simulation of a stochastic differential equation (SDE) version of the Medtronic Virtual Patient (MVP) model including process and output noise. We compare the fits with the actual CGM signal, as well as the short- and long-term predictions for each identified model.
The numerical results show that the maximum likelihood-based identification techniques offer the best performance in terms of fitting and prediction. Moreover, they have other advantages compared to ODE-based modeling, such as parameter tracking, population modeling and handling of outliers.
Language: | English |
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Publisher: | Elsevier BV |
Year: | 2016 |
Pages: | 759-764 |
Proceedings: | 11th IFAC Symposium on Dynamics and Control of Process Systems Including Biosystems DYCOPS-CAB 2016 |
ISSN: | 24058963 and 24058971 |
Types: | Conference paper and Journal article |
DOI: | 10.1016/j.ifacol.2016.07.279 |
ORCIDs: | Boiroux, Dimitri , Mahmoudi, Zeinab , Poulsen, Niels Kjølstad , Madsen, Henrik and Jørgensen, John Bagterp |