Nonlinear modeling of the dynamic effects of infused insulin on glucose: comparison of compartmental with Volterra models.

Mitsis, GD; Markakis, MG; Marmarelis, VZ

dc.contributor.author	Mitsis, GD	en
dc.contributor.author	Markakis, MG	en
dc.contributor.author	Marmarelis, VZ	en
dc.date.accessioned	2014-03-01T01:31:21Z
dc.date.available	2014-03-01T01:31:21Z
dc.date.issued	2009	en
dc.identifier.issn	0018-9294	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19789
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-74049153277&partnerID=40&md5=8718916df5ab9c70538e6d7a982f1077	en
dc.subject	Laguerre-Volterra networks (LVNs)	en
dc.subject	physiological systems	en
dc.subject	Volterra-Wiener models	en
dc.subject.classification	Engineering, Biomedical	en
dc.subject.other	insulin	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	artificial intelligence	en
dc.subject.other	biological model	en
dc.subject.other	computer simulation	en
dc.subject.other	glucose blood level	en
dc.subject.other	human	en
dc.subject.other	metabolism	en
dc.subject.other	nonlinear system	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial Intelligence	en
dc.subject.other	Blood Glucose	en
dc.subject.other	Computer Simulation	en
dc.subject.other	Humans	en
dc.subject.other	Insulin	en
dc.subject.other	Models, Biological	en
dc.subject.other	Nonlinear Dynamics	en
dc.title	Nonlinear modeling of the dynamic effects of infused insulin on glucose: comparison of compartmental with Volterra models.	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	This paper presents the results of a computational study that compares simulated compartmental (differential equation) and Volterra models of the dynamic effects of insulin on blood glucose concentration in humans. In the first approach, we employ the widely accepted ""minimal model"" and an augmented form of it, which incorporates the effect of insulin secretion by the pancreas, in order to represent the actual closed-loop operating conditions of the system, and in the second modeling approach, we employ the general class of Volterra-type models that are estimated from input-output data. We demonstrate both the equivalence between the two approaches analytically and the feasibility of obtaining accurate Volterra models from insulin-glucose data generated from the compartmental models. The results corroborate the proposition that it may be preferable to obtain data-driven (i.e., inductive) models in a more general and realistic operating context, without resorting to the restrictive prior assumptions and simplifications regarding model structure and/or experimental protocols (e.g., glucose tolerance tests) that are necessary for the compartmental models proposed previously. These prior assumptions may lead to results that are improperly constrained or biased by preconceived (and possibly erroneous) notions-a risk that is avoided when we let the data guide the inductive selection of the appropriate model within the general class of Volterra-type models, as our simulation results suggest.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE transactions on bio-medical engineering	en
dc.identifier.isi	ISI:000269838000001	en
dc.identifier.volume	56	en
dc.identifier.issue	10	en
dc.identifier.spage	2347	en
dc.identifier.epage	2358	en