Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets

Kristin M. Tolle, Hsinchun Chen, Hsiao Hui Chow

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Predicting blood concentration levels of pharmaceutical agents in human subjects can be made difficult by missing data and variability within and between human subjects. Biometricians use a variety of software tools to analyze pharmacokinetic information in order to conduct research about a pharmaceutical agent. This paper is the comparison between using a feedforward backpropagation neural network to predict blood serum concentration levels of the drug tobramycin in pediatric cystic fibrosis and hemotologic-oncologic disorder patients with the most commonly used software for analysis of pharmacokinetics, NONMEM. Mean squared standard error is used to establish the comparability of the two estimation methods. The motivation for this research is the desire to provide clinicians and pharmaceutical researchers a cost effective, user friendly, and timely analysis tool for effectively predicting blood concentration ranges in human subjects.

Original languageEnglish (US)
Pages (from-to)139-151
Number of pages13
JournalDecision Support Systems
Volume30
Issue number2
DOIs
StatePublished - Dec 27 2000

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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