title: Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques.
abstract: 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.
authors: IDAL, Intelligent Data Analysis Laboratory, University of Valencia, Electronic Engineering Department, Av de la Universidad, s/n, Burjassot, 46100 Valencia, Spain. Electronic address: jose.maria.martinez@uv.es.
authors: José M
authors: JM
authors: Martínez-Martínez
authors: IDAL, Intelligent Data Analysis Laboratory, University of Valencia, Electronic Engineering Department, Av de la Universidad, s/n, Burjassot, 46100 Valencia, Spain.
authors: Pablo
authors: P
authors: Escandell-Montero
authors: Fresenius Medical Care, Bad Homburg, Germany.
authors: Carlo
authors: C
authors: Barbieri
authors: IDAL, Intelligent Data Analysis Laboratory, University of Valencia, Electronic Engineering Department, Av de la Universidad, s/n, Burjassot, 46100 Valencia, Spain.
authors: Emilio
authors: E
authors: Soria-Olivas
authors: Fresenius Medical Care, Bad Homburg, Germany.
authors: Flavio
authors: F
authors: Mari
authors: IDAL, Intelligent Data Analysis Laboratory, University of Valencia, Electronic Engineering Department, Av de la Universidad, s/n, Burjassot, 46100 Valencia, Spain.
authors: Marcelino
authors: M
authors: Martínez-Sober
authors: Fresenius Medical Care, Bad Homburg, Germany.
authors: Claudia
authors: C
authors: Amato
authors: IDAL, Intelligent Data Analysis Laboratory, University of Valencia, Electronic Engineering Department, Av de la Universidad, s/n, Burjassot, 46100 Valencia, Spain.
authors: Antonio J
authors: AJ
authors: Serrano López
authors: Fresenius Medical Care, Bad Homburg, Germany.
authors: Marcello
authors: M
authors: Bassi
authors: IDAL, Intelligent Data Analysis Laboratory, University of Valencia, Electronic Engineering Department, Av de la Universidad, s/n, Burjassot, 46100 Valencia, Spain.
authors: Rafael
authors: R
authors: Magdalena-Benedito
authors: Fresenius Medical Care, Bad Homburg, Germany.
authors: Andrea
authors: A
authors: Stopper
authors: IDAL, Intelligent Data Analysis Laboratory, University of Valencia, Electronic Engineering Department, Av de la Universidad, s/n, Burjassot, 46100 Valencia, Spain.
authors: José D
authors: JD
authors: Martín-Guerrero
authors: Fresenius Medical Care, Bad Homburg, Germany; Department of Clinical Medicine and Biotechnology, Danube University Krems, Austria.
authors: Emanuele
authors: E
authors: Gatti
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000740:Anemia / Q000097:blood* / Q000175:diagnosis / Q000188:drug therapy*; D001185:Artificial Intelligence; D015415:Biomarkers / Q000097:blood; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D016903:Drug Monitoring / Q000379:methods*; D004360:Drug Therapy, Computer-Assisted / Q000379:methods; D004921:Erythropoietin / Q000008:administration & dosage*; D005260:Female; D006454:Hemoglobins / Q000032:analysis*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D006435:Renal Dialysis / Q000009:adverse effects* / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D015415:Biomarkers; D006454:Hemoglobins; D004921:Erythropoietin
keywords: Chronic renal failure; Hemodialysis; Hemoglobin; Machine learning; Prediction