Pharmacodynamic population analysis in chronic renal failure using artificial neural networks - A comparative study

Neural Networks

Volumn 16, Issue 5-6, 2003, Pages 841-845

  Gaweda, Adam E ^a   Jacobs, Alfred A ^a   Brier, Michael E ^a   Zurada, Jacek M ^b  

^a UNIVERSITY OF LOUISVILLE   (United States)

^b University of Louisville   (United States)

Author keywords

Autoregressive model; Multilayer perceptron network; Pharmacodynamic modeling; Radial basis

Indexed keywords

COST EFFECTIVENESS; DRUG DOSAGE; PATIENT TREATMENT; PHARMACODYNAMICS;

POPULATION ANALYSIS;

NEURAL NETWORKS;

ADULT; ARTIFICIAL NEURAL NETWORK; CHRONIC KIDNEY FAILURE; COMPUTER MODEL; COMPUTER PREDICTION; CONFERENCE PAPER; CONTROLLED STUDY; COST EFFECTIVENESS ANALYSIS; DOSE TIME EFFECT RELATION; DRUG COST; DRUG DISTRIBUTION; DRUG DOSE REGIMEN; DRUG RESPONSE; FEMALE; HUMAN; HUMAN COMPUTER INTERACTION; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; POPULATION RESEARCH; PRIORITY JOURNAL;

EID: 0038355073     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(03)00084-4     Document Type: Conference Paper

References (10)

1. Bellazzi R. Drug delivery optimization through Bayesian networks: an application to erythropoietin therapy in uremic anemia. Computers and Biomedical Research. 26:1992;274-293.
2. Bellazzi R., Siviero C., Bellazzi R. Mathematical modeling of erythropoietin therapy in uremic anemia. Does it improve cost-effectiveness? Haematologica. 79:1994;154-164.
3. Brier M.E., Zurada J.M., Aronoff G.R. Neural network predicted peak and trough gentamicin concentrations. Pharmaceutical Research. 12:(3):1995;406-412.
4. Fisher J.W. Erythropoietin: physiology and pharmacology update. Experimental Biology and Medicine. 228:(1):2003;1-14.
5. Guerrero J.D.M., Olivas E.S., Camps Valls G., Serrano Lopez A.J., Perez Ruixo J.J., Jimenez Torres N.V. Use of neural networks for dosage individualisation of erythropoietin in patients with secondary anemia to chronic renal failure. Computers in Biology and Medicine. 2003;. in press.
6. Jacobs A.A., Lada P., Zurada J.M., Brier M.E., Aronoff G.R. Predictors of hematocrit in hemodialysis patients as determined by artificial neural networks. Journal of American Society of Nephrology. 12:2001;p. 387A. (Program and Abstract issue).
7. Jang J.R., Sun C. Functional equivalence between radial basis function and fuzzy inference systems. IEEE Transactions on Neural Networks. 4:1993;156-159.
8. Kosko B. Optimal fuzzy rules cover extrema. International Journal of Intelligent Systems. 10:(2):1995;249-255.
9. Veng-Pedersen P., Modi N.B. Neural networks in pharmacodynamic modeling. Is current modeling practice of complex kinetic systems at a dead end? Journal of Pharmacokinetics and Biophamraceutics. 20:(4):1992;397-412.
10. Veng-Pedersen P., Modi N.B. Application of neural networks in pharmacodynamics. D'Argenio D.Z. Advanced methods of pharmacokinetic and pharmacodynamic systems analysis. 2:1995;101-120 Plenum Press, New York.