Pharmacodynamic population analysis in chronic renal failure using artificial neural networks: a comparative study

Authors:
Adam E. Gaweda;Alfred A. Jacobs;Michael E. Brier;Jacek M. Zurada
Affiliations:
Kidney Disease Program, University of Louisville, Louisville, KY;Kidney Disease Program, University of Louisville, Louisville, KY;Kidney Disease Program, University of Louisville, Louisville, KY and Department of Veteran Affairs, University of Louisville, Louisville, KY;Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

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Abstract

This work presents a pharmacodynamic population analysis in chronic renal failure patients using Artificial Neural Networks (ANNs). In pursuit of an effective and cost-efficient strategy for drug delivery in patients with renal failure, two different types of ANN are applied to perform drug dose-effect modeling and their performance compared. Applied in a clinical environment, such models will allow for prediction of patient response to the drug at the effect site and, subsequently, for adjusting the dosing regimen.