@article{8603a4f5cda64dceb6c575aaad4d4bda,
title = "Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets",
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.",
author = "Tolle, {Kristin M.} and Hsinchun Chen and Chow, {Hsiao Hui}",
note = "Funding Information: Kristin M. Tolle is a PhD candidate of Management Information Systems at the University of Arizona where she received her MS in MIS (1997). She is also a senior member and research associate of the UA/MIS Artificial Intelligence Lab. She received her BS in Computer Information Systems from Boise State University (1988). Tolle is a recipient of a research fellowship from the National Library of Medicine and the Oak Ridge Institute for Science and Education. She has published several journal and conference articles on topics ranging from medical information retrieval, natural language processing, intelligent agents and neural network medical decision support systems. Funding Information: This project was supported in part by the following grants: Special Information Services, National Library of Medicine (NLM), National Institutes of Health (NIH), “Semantic Retrieval for Toxicology and Hazardous Substance Databases”, 1996–1997; National Cancer Institute (NCI), National Institutes of Health (NIH), “Information Analysis and Visualization for Cancer Literature”, 1996–1997; and AT&T Foundation Special Purpose Grants in Science and Engineering, 1994–1996. ",
year = "2000",
month = dec,
day = "27",
doi = "10.1016/S0167-9236(00)00094-4",
language = "English (US)",
volume = "30",
pages = "139--151",
journal = "Decision Support Systems",
issn = "0167-9236",
publisher = "Elsevier B.V.",
number = "2",
}