6533b823fe1ef96bd127ee26

RESEARCH PRODUCT

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

Antonio J. Serrano-lópezJosé D. Martín-guerreroN.v. Jimenez-torresGustau Camps-vallsEmilio Soria-olivasJuan Jose Perez-ruixo

subject

AdultAnemia HemolyticInjections SubcutaneousAutoregressive conditional heteroskedasticityBiomedical EngineeringMachine learningcomputer.software_genreCohort StudiesHemoglobinsRenal DialysisFeature (machine learning)HumansMedicineSensitivity (control systems)Time seriesErythropoietinAgedAged 80 and overArtificial neural networkbusiness.industryMiddle AgedRecombinant ProteinsRegressionDrug Therapy Computer-AssistedRegression PsychologySupport vector machineTreatment OutcomeMultilayer perceptronKidney Failure ChronicNeural Networks ComputerArtificial intelligencebusinesscomputerAlgorithmsBiomedical engineering

description

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 /spl epsiv/-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.

yearjournalcountryeditionlanguage
2003-10-01IEEE Transactions on Biomedical Engineering
https://doi.org/10.1109/tbme.2003.816084