The precision of identified variables with respect to multivariable set size in glycaemic data from a virtual type 1 diabetic patient

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Title

The precision of identified variables with respect to multivariable set size in glycaemic data from a virtual type 1 diabetic patient

Content partner

University of Canterbury Library

Collection

UC Research Repository

Description

Prior research had been carried out to identify a large number of glycaemic variables in sparse, noisy data from a virtual diabetic patient. This paper investigates the precision of variables as an identification scheme introduces progressively more parameters into the variable set and as the quantity of data increases. Virtual data was simulated with a diabetic glycaemic meal model that contained six variable parameters. Data was sampled 6 times daily with noise. Increasing variable sets wer...

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Format

Research paper

Research format

Conference item

Date created

2015

Creator

Mansell, E.J. / Docherty, P.D. / Chase, Geoff

URL

http://hdl.handle.net/10092/11030

Related subjects

parameter identification / identifiability / physiological models / Monte Carlo simulation / type 1 diabetes / stress hyperglycaemia / exercise / Engineering / Biomedical engineering / Biomechanical engineering

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