Literature DB >> 12850042

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

Adam E Gaweda1, Alfred A Jacobs, Michael E Brier, Jacek M Zurada.   

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.

Entities:  

Mesh:

Year:  2003        PMID: 12850042     DOI: 10.1016/S0893-6080(03)00084-4

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  6 in total

1.  Dose adjustment in renal impairment: response from Drug Prescribing in Renal Failure.

Authors:  George R Aronoff
Journal:  BMJ       Date:  2005-07-30

2.  Population pharmacokinetic and pharmacodynamic models of remifentanil in healthy volunteers using artificial neural network analysis.

Authors:  S H Kang; M R Poynton; K M Kim; H Lee; D H Kim; S H Lee; K S Bae; O Linares; S E Kern; G J Noh
Journal:  Br J Clin Pharmacol       Date:  2007-02-23       Impact factor: 4.335

Review 3.  Predictive modeling for improved anemia management in dialysis patients.

Authors:  Michael E Brier; Adam E Gaweda
Journal:  Curr Opin Nephrol Hypertens       Date:  2011-11       Impact factor: 2.894

4.  Dosage individualization of warfarin using artificial neural networks.

Authors:  Mohammad I Saleh; Sameh Alzubiedi
Journal:  Mol Diagn Ther       Date:  2014-06       Impact factor: 4.074

5.  Randomized trial of model predictive control for improved anemia management.

Authors:  Michael E Brier; Adam E Gaweda; Andrew Dailey; George R Aronoff; Alfred A Jacobs
Journal:  Clin J Am Soc Nephrol       Date:  2010-02-25       Impact factor: 8.237

6.  Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness?

Authors:  Luca Gabutti; Nathalie Lötscher; Josephine Bianda; Claudio Marone; Giorgio Mombelli; Michel Burnier
Journal:  BMC Nephrol       Date:  2006-09-18       Impact factor: 2.388

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.