Model Identification using Continuous Glucose Monitoring Data for Type 1 Diabetes

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Abstract

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.
Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume49
Issue number7
Pages (from-to)759-764
ISSN2405-8963
DOIs
Publication statusPublished - 2016
Event11th IFAC Symposium on Dynamics and Control of Process Systems Including Biosystems DYCOPS-CAB 2016 - Trondheim, Norway
Duration: 6 Jun 20168 Jun 2016
Conference number: 11
http://dycops2016.org/

Conference

Conference11th IFAC Symposium on Dynamics and Control of Process Systems Including Biosystems DYCOPS-CAB 2016
Number11
CountryNorway
CityTrondheim
Period06/06/201608/06/2016
Internet address

Keywords

  • Type 1 diabetes
  • Parameter identification
  • Continuous glucose monitoring
  • Least squares
  • Huber regression
  • Maximum likelihood

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