Title :
Connectionist modeling vs. Bayesian procedures for sparse data pharmacokinetic parameter estimation
Author :
Shadmehr, Reza ; D´Argenio, David Z.
Author_Institution :
Univ. of Southern California, Los Angeles, CA, USA
Abstract :
A connectionist model (adaptive neural network) is developed for estimating the pharmacokinetic properties of a drug from plasma concentrations measured during the course of therapy. The back-propagation algorithm was used to determine the weights in a three-layered network model from simulated sets of kinetic parameters and drug concentrations. The estimation performance of the connectionist model is shown to compare well to that of maximum-likelihood and Bayesian estimators
Keywords :
Bayes methods; parameter estimation; physiological models; 3-layered network model; Bayesian estimator; adaptive neural network; back-propagation algorithm; connectionist model; estimation performance; kinetic parameters; maximum-likelihood estimator; plasma concentration; sparse data pharmacokinetic parameter estimation; Adaptive systems; Bayesian methods; Drugs; Kinetic theory; Maximum likelihood estimation; Medical treatment; Neural networks; Plasma measurements; Plasma properties; Plasma simulation;
Conference_Titel :
Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in
Conference_Location :
Seattle, WA
DOI :
10.1109/IEMBS.1989.96593
