Literature DB >> 14560766

Dosage individualization of erythropoietin using a profile-dependent support vector regression.

José David Martín-Guerrero1, Gustavo Camps-Valls, Emilio Soria-Olivas, Antonio José Serrano-López, Juan José Pérez-Ruixo, Nicolas Víctor Jiménez-Torres.   

Abstract

The external administration of recombinant human erythropoietin is the chosen treatment for those patients with secondary anemia due to chronic renal failure in periodic hemodialysis. The objective of this paper is to carry out an individualized prediction of the EPO dosage to be administered to those patients. The high cost of this medication, its side-effects and the phenomenon of potential resistance which some individuals suffer all justify the need for a model which is capable of optimizing dosage individualization. A group of 110 patients and several patient factors were used to develop the models. The support vector regressor (SVR) is benchmarked with the classical multilayer perceptron (MLP) and the Autoregressive Conditional Heteroskedasticity (ARCH) model. We introduce a priori knowledge by relaxing or tightening the epsilon-insensitive region and the penalization parameter depending on the time period of the patients' follow-up. The so-called profile-dependent SVR (PD-SVR) improves results of the standard SVR method and the MLP. We perform sensitivity analysis on the MLP and inspect the distribution of the support vectors in the input and feature spaces in order to gain knowledge about the problem.

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Year:  2003        PMID: 14560766     DOI: 10.1109/TBME.2003.816084

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  9 in total

1.  Individualized drug dosing using RBF-Galerkin method: Case of anemia management in chronic kidney disease.

Authors:  Hossein Mirinejad; Adam E Gaweda; Michael E Brier; Jacek M Zurada; Tamer Inanc
Journal:  Comput Methods Programs Biomed       Date:  2017-06-23       Impact factor: 5.428

2.  Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence.

Authors:  Maurizio Sessa; Abdul Rauf Khan; David Liang; Morten Andersen; Murat Kulahci
Journal:  Front Pharmacol       Date:  2020-07-16       Impact factor: 5.810

3.  Minding impacting events in a model of stochastic variance.

Authors:  Sílvio M Duarte Queirós; Evaldo M F Curado; Fernando D Nobre
Journal:  PLoS One       Date:  2011-03-31       Impact factor: 3.240

4.  Computerized decision support for EPO dosing in hemodialysis patients.

Authors:  Dana C Miskulin; Daniel E Weiner; Hocine Tighiouart; Vladimir Ladik; Karen Servilla; Philip G Zager; Alice Martin; H K Johnson; Klemens B Meyer
Journal:  Am J Kidney Dis       Date:  2009-09-25       Impact factor: 8.860

5.  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

Review 6.  Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.

Authors:  Alexandru Burlacu; Adrian Iftene; Daniel Jugrin; Iolanda Valentina Popa; Paula Madalina Lupu; Cristiana Vlad; Adrian Covic
Journal:  Biomed Res Int       Date:  2020-06-10       Impact factor: 3.411

7.  Profiled support vector machines for antisense oligonucleotide efficacy prediction.

Authors:  Gustavo Camps-Valls; Alistair M Chalk; Antonio J Serrano-López; José D Martín-Guerrero; Erik L L Sonnhammer
Journal:  BMC Bioinformatics       Date:  2004-09-22       Impact factor: 3.169

8.  Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients.

Authors:  Carlo Barbieri; Elena Bolzoni; Flavio Mari; Isabella Cattinelli; Francesco Bellocchio; José D Martin; Claudia Amato; Andrea Stopper; Emanuele Gatti; Iain C Macdougall; Stefano Stuard; Bernard Canaud
Journal:  PLoS One       Date:  2016-03-03       Impact factor: 3.240

Review 9.  Role of Artificial Intelligence in Kidney Disease.

Authors:  Qiongjing Yuan; Haixia Zhang; Tianci Deng; Shumei Tang; Xiangning Yuan; Wenbin Tang; Yanyun Xie; Huipeng Ge; Xiufen Wang; Qiaoling Zhou; Xiangcheng Xiao
Journal:  Int J Med Sci       Date:  2020-04-06       Impact factor: 3.738

  9 in total

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