Document Detail

Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques.
MedLine Citation:
PMID:  25070755     Owner:  NLM     Status:  Publisher    
Patients who suffer from chronic renal failure (CRF) tend to suffer from an associated anemia as well. Therefore, it is essential to know the hemoglobin (Hb) levels in these patients. The aim of this paper is to predict the hemoglobin (Hb) value using a database of European hemodialysis patients provided by Fresenius Medical Care (FMC) for improving the treatment of this kind of patients. For the prediction of Hb, both analytical measurements and medication dosage of patients suffering from chronic renal failure (CRF) are used. Two kinds of models were trained, global and local models. In the case of local models, clustering techniques based on hierarchical approaches and the adaptive resonance theory (ART) were used as a first step, and then, a different predictor was used for each obtained cluster. Different global models have been applied to the dataset such as Linear Models, Artificial Neural Networks (ANNs), Support Vector Machines (SVM) and Regression Trees among others. Also a relevance analysis has been carried out for each predictor model, thus finding those features that are most relevant for the given prediction.
José M Martínez-Martínez; Pablo Escandell-Montero; Carlo Barbieri; Emilio Soria-Olivas; Flavio Mari; Marcelino Martínez-Sober; Claudia Amato; Antonio J Serrano López; Marcello Bassi; Rafael Magdalena-Benedito; Andrea Stopper; José D Martín-Guerrero; Emanuele Gatti
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-7-14
Journal Detail:
Title:  Computer methods and programs in biomedicine     Volume:  -     ISSN:  1872-7565     ISO Abbreviation:  Comput Methods Programs Biomed     Publication Date:  2014 Jul 
Date Detail:
Created Date:  2014-7-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8506513     Medline TA:  Comput Methods Programs Biomed     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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