Therapeutic drug dosing prediction using adaptive models and artificial neural networks

Author

Lada, Peter ; Brier, Micheal E. ; Zurada, Jacek M.

Author_Institution

Dept. of Electr. Eng., Louisville Univ., KY, USA

Volume

5

fYear

1999

fDate

1999

Firstpage

3669

Abstract

Using process control approaches, pharmacokinetic models are developed. All models presented are based on the one-compartment model. These neural network-based and adaptive versions of linear, nonlinear, and time-dependent models are tested on data sets collected from hemodialysis patients receiving anti-coagulant heparin during treatment. Results show that increased model complexity ensures improved quality of identification, while it decreases the initial quality of estimation

Keywords

feedforward neural nets; learning (artificial intelligence); patient treatment; adaptive models; anti-coagulant heparin; hemodialysis patients; linear models; model complexity; nonlinear models; one-compartment model; pharmacokinetic models; process control approaches; therapeutic drug dosing prediction; time-dependent models; Artificial neural networks; Blood; Drugs; Equations; Feedforward neural networks; Neural networks; Plasma applications; Predictive models; Process control; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.836266

Filename

836266