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

Reza Shadmehr, David Z. D'Argenio

Research output: Contribution to journalConference articlepeer-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 languageEnglish (US)
Pages (from-to)2058-2059
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume11 pt 6
StatePublished - Dec 1 1989
Externally publishedYes
EventImages 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 1989Nov 12 1989

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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