title: Artificial intelligence for optimal anemia management in end-stage renal disease.
abstract: Computational intelligence for the prediction of hemoglobin to guide the selection of erythropoiesis-stimulating agent dose results in improved anemia management. The models used for the prediction result from the use of individual patient data and help to increase the number of hemoglobin observations within the target range. The benefits of using these modeling techniques appear to be a decrease in erythropoiesis-stimulating agent use and a decrease in the number of transfusions. This study confirms the results of previous smaller studies and suggests that additional beneficial results may be achieved.
authors.affiliation: Division of Nephrology and Hypertension, Department of Medicine, University of Louisville, Kentucky, USA. Electronic address: mbrier@louisville.edu.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Brier
authors.affiliation: Division of Nephrology and Hypertension, Department of Medicine, University of Louisville, Kentucky, USA.
authors.forename: Adam E
authors.initials: AE
authors.lastname: Gaweda
mesh_terms: D000740:Anemia; D001185:Artificial Intelligence; D004921:Erythropoietin; D006397:Hematinics; D006454:Hemoglobins; D006801:Humans; D007676:Kidney Failure, Chronic; D006435:Renal Dialysis
chemical_list: D006397:Hematinics; D006454:Hemoglobins; D004921:Erythropoietin