Connectionist modeling vs. Bayesian procedures for sparse data pharmacokinetic parameter estimation

Reza Shadmehr; David Z. D'Argenio

Connectionist modeling vs. Bayesian procedures for sparse data pharmacokinetic parameter estimation

Reza Shadmehr, David Z. D'Argenio

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English (US)
Pages (from-to)	2058-2059
Number of pages	2
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	11 pt 6
State	Published - Dec 1 1989
Externally published	Yes
Event	Images of the Twenty-First Century - Proceedings of the 11th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 2 - Seattle, WA, USA Duration: Nov 9 1989 → Nov 12 1989

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Cite this

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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.",

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AB - 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.

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SN - 0589-1019

VL - 11 pt 6

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JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - Images of the Twenty-First Century - Proceedings of the 11th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 2

Y2 - 9 November 1989 through 12 November 1989

ER -

Connectionist modeling vs. Bayesian procedures for sparse data pharmacokinetic parameter estimation

Abstract

ASJC Scopus subject areas

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