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Identification of intra-patient variability in the postprandial response of patients with type 1 diabetes

Identification of individualized models for patients with type 1 diabetes is of vital importance for the development of a successful artificial pancreas and other model-based strategies of insulin treatment. However, the huge intra-patient glycemic variability frequently prevents the identification of reliable models, especially in the postprandial period. In this work, the identification of postprandial models characterizing intra-patient variability is addressed. Methods: Regarding the postprandial response, uncertainties due to physiological variability, input errors in insulin infusion rate and in meal content estimation are characterized by means of interval models, which predict a glucose envelope containing all possible patient responses according to the model. Multi-objective optimization is performed over a cohort of virtual patients, minimizing both the fitting error and the output glucose envelope width. A Pareto Front is then built ranging from classic identification representing average behaviors to interval identification guaranteeing full enclosure of the measurements. A method for the selection of the best individual in the Pareto Front for identification from home monitoring data with a continuous glucose monitor is presented, reducing the overestimation of patient’s variability due to monitor inaccuracies and noise. Results: Identification using glucose reference data provide model bands that accurately fit all data points in the used virtual data set. Identification from continuous glucose monitor data, using two different width estimation procedures yield very similar prediction capabilities of around 60% of the data points predicted, and less than a 5% average error. Conclusions: In this work, a new approach to evaluate intra-patient variability in the identification of postprandial models is presented. The proposed method is feasible and shows good prediction capabilities in a 5-h time horizon as compared to reference measurements

This work received funding from the Spanish Ministry of Science and Innovation under grant DPI2010-20764-C02, from the Generalitat Valenciana under project GV/2012/085, and from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement FP7-PEOPLE-2009-IEF, Ref 252085.

© Biomedical Signal Processing and Control, 2014, vol. 12, p. 39-46

Elsevier

Author: Laguna Sanz, Alejandro José
Rossetti, Paolo
Ampudia Blasco, F. Javier
Vehí, Josep
Bondía Company, Jorge
Date: 2014 July
Abstract: Identification of individualized models for patients with type 1 diabetes is of vital importance for the development of a successful artificial pancreas and other model-based strategies of insulin treatment. However, the huge intra-patient glycemic variability frequently prevents the identification of reliable models, especially in the postprandial period. In this work, the identification of postprandial models characterizing intra-patient variability is addressed. Methods: Regarding the postprandial response, uncertainties due to physiological variability, input errors in insulin infusion rate and in meal content estimation are characterized by means of interval models, which predict a glucose envelope containing all possible patient responses according to the model. Multi-objective optimization is performed over a cohort of virtual patients, minimizing both the fitting error and the output glucose envelope width. A Pareto Front is then built ranging from classic identification representing average behaviors to interval identification guaranteeing full enclosure of the measurements. A method for the selection of the best individual in the Pareto Front for identification from home monitoring data with a continuous glucose monitor is presented, reducing the overestimation of patient’s variability due to monitor inaccuracies and noise. Results: Identification using glucose reference data provide model bands that accurately fit all data points in the used virtual data set. Identification from continuous glucose monitor data, using two different width estimation procedures yield very similar prediction capabilities of around 60% of the data points predicted, and less than a 5% average error. Conclusions: In this work, a new approach to evaluate intra-patient variability in the identification of postprandial models is presented. The proposed method is feasible and shows good prediction capabilities in a 5-h time horizon as compared to reference measurements
This work received funding from the Spanish Ministry of Science and Innovation under grant DPI2010-20764-C02, from the Generalitat Valenciana under project GV/2012/085, and from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement FP7-PEOPLE-2009-IEF, Ref 252085.
Format: application/pdf
ISSN: 1746-8094
Document access: http://hdl.handle.net/10256/11729
Language: eng
Publisher: Elsevier
Collection: MICINN/PN 2011-2013/DPI2010-20764-C02
Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.bspc.2013.07.003
Articles publicats (D-EEEiA)
Is part of: © Biomedical Signal Processing and Control, 2014, vol. 12, p. 39-46
Rights: Tots els drets reservats
Subject: Control, Teoria de
Control theory
Estimació de paràmetres
Parameter estimation
Anàlisi de sistemes
System analysis
Anàlisi d’intervals (Matemàtica)
Interval analysis (Mathematics)
Diabetis
Diabetes
Control intel·ligent
Intelligent control systems
Title: Identification of intra-patient variability in the postprandial response of patients with type 1 diabetes
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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