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Bayesian Identification of a Population Compartmental Model

By Of Peptide Kinetics, Paolo Magni, Riccardo Bellazzi, Giovanni Sparacino and Claudio Cobelli

Abstract

When models are used to measure or predict physiological variables and parameters in a given individual, the experiments needed are often complex and costly. A valuable solution for improving their cost effectiveness is represented by population models. A widely used population model in insulin secretion studies is the one proposed by Van Cauter et al. #Diabetes 41:368--377, 1992#, which determines the parameters of the two compartment model of C-peptide kinetics in a given individual from the knowledge of his/her age, sex, body surface area, and health condition #i.e., normal, obese, diabetic#. This population model was identified from the data of a large training set #more than 200 subjects# via a deterministic approach. This approach, while sound in terms of providing a point estimate of C-peptide kinetic parameters in a given individual, does not provide a measure of their precision. In this paper, by employing the same training set of Van Cauter et al., we show that the identification of the population model into a Bayesian framework #by using Markov chain Monte Carlo# allows, at the individual level, the estimation of point values of the C-peptide kinetic parameters together with their precision. A successful application of the methodology is illustrated in the estimation of C-peptide kinetic parameters of seven subjects #not belonging to the training set used for the identification of the population model# for which reference values were available thanks to an independent identification experiment. 2000 Biomedical Engineering Society. #S0090-6964#00#00907-3# Keywords---C-peptide, Population model, Compartmental model, Bayes estimation, Markov chain Monte Carlo, Insulin, System identificatio

Topics: Population model, Compartmental model, Bayes estimation, Markov chain Monte Carlo, Insulin, System identification
Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.18.6261
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