Nonlinear modeling of glucose metabolism: Comparison of parametric vs. nonparametric methods

Mitsis, GD; Marmarelis, VZ

dc.contributor.author	Mitsis, GD	en
dc.contributor.author	Marmarelis, VZ	en
dc.date.accessioned	2014-03-01T02:51:08Z
dc.date.available	2014-03-01T02:51:08Z
dc.date.issued	2007	en
dc.identifier.issn	05891019	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35390
dc.subject.other	Computational methods	en
dc.subject.other	Computer simulation	en
dc.subject.other	Constraint theory	en
dc.subject.other	Metabolism	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Data guide	en
dc.subject.other	Inductive selection	en
dc.subject.other	Nonparametric models	en
dc.subject.other	Glucose	en
dc.title	Nonlinear modeling of glucose metabolism: Comparison of parametric vs. nonparametric methods	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2007.4353707	en
heal.identifier.secondary	4353707	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2007.4353707	en
heal.publicationDate	2007	en
heal.abstract	This paper presents the results of computational studies that compare simulated parametric and nonparametric models in terms of their ability to obtain reliable quantitative descriptions of the dynamic effects of variable infusions of insulin on blood glucose concentration in human subjects. In the nonparametric modeling approach, we employ the general class of Volterra-type models that are estimated from input-output data. The parametric models considered are the extensively studied ""minimal model"" and an augmented variant of the latter that incorporates the process of insulin secretion by the pancreas in response to elevated blood glucose. This model represents the actual closed-loop operating conditions of the system. The presented results demonstrate the feasibility of obtaining data-driven (i.e. inductive) nonparametric models in a realistic operating context, without resorting to the restrictive prior assumptions of model structure that are necessary for the numerous parametric (compartmental) models proposed previously. The rationale underpinning the nonparametric approach is that prior assumptions regarding the model structure may lead to results that are improperly constrained or biased by preconceived notions. Thus, it may be preferable to let the data guide the inductive selection of the appropriate model within the general class of Volterra-type models that imposes no such constraints. © 2007 IEEE.	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en
dc.identifier.doi	10.1109/IEMBS.2007.4353707	en
dc.identifier.spage	5967	en
dc.identifier.epage	5970	en