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

Persistent Link:
http://hdl.handle.net/10150/105955
Title:
Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets
Author:
Tolle, Kristin M.; Chen, Hsinchun; Chow, Hsiao-Hui
Citation:
Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets 2000, 30(2):139-152 Decision Support Systems, Special Issue on Decision Support for Heath Care in a New Information Age
Publisher:
Elsevier
Journal:
Decision Support Systems, Special Issue on Decision Support for Heath Care in a New Information Age
Issue Date:
2000
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105955
Submitted date:
2004-10-13
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 hemotologicâ 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.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Medical applications

Full metadata record

DC FieldValue Language
dc.contributor.authorTolle, Kristin M.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorChow, Hsiao-Huien_US
dc.date.accessioned2004-10-13T00:00:01Z-
dc.date.available2010-06-18T23:37:19Z-
dc.date.issued2000en_US
dc.date.submitted2004-10-13en_US
dc.identifier.citationEstimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets 2000, 30(2):139-152 Decision Support Systems, Special Issue on Decision Support for Heath Care in a New Information Ageen_US
dc.identifier.urihttp://hdl.handle.net/10150/105955-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractPredicting 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 hemotologicâ 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectArtificial Intelligenceen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherMedical applicationsen_US
dc.titleEstimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data setsen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalDecision Support Systems, Special Issue on Decision Support for Heath Care in a New Information Ageen_US
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