PUMA
Istituto di Ingegneria Biomedica     
Morbiducci U., Tura A., Grigioni M. Genetic algorithms for parameter estimation in mathematical modeling of glucose metabolism. In: Computers in Biology and Medicine, vol. 35 (10) pp. 862 - 74. Elsevier, 2005.
 
 
Abstract
(English)
Direct measurement of hormones secretion and kinetics in glucose metabolism is not feasible in the clinical practice, being highly invasive. As their knowledge is important in the diagnosis of metabolic disorders, thanks to mathematical models based on non-invasive tests, estimation of hormones behaviour is obtained. Unfortunately, traditional model estimation can suffer for convergence problems, and it can be strongly dependent on the parameters initial value. To overcome these limitations, Genetic algorithms (GAs) were tested on a group of 49 subjects. The stochastic nature of GAs allowed overcoming the initialization problem. Moreover, GAs significantly improved the accuracy of fit.
URL: http://www.ncbi.nlm.nih.gov/pubmed/16310011?itool=EntrezSystem2.PEntrez.Pubmed.PuVDocSum&ordinalpos=2bmed_ResultsPanel.Pubmed_R
DOI: 10.1016/j.compbiomed.2004.07.005
Subject mathematical modeling
Genetic algorithms
Insulin secretion
Insulin kinetics
Pancreatic hormones


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional