Author:
Rogg Sabrina,Kotanko Peter
Reference45 articles.
1. Barbieri C (2016) Anemia management in end-stage renal disease patients undergoing dialysis: a comprehensive approach through machine learning techniques and mathematical modeling. PhD Thesis, University of Valencia, Valencia, Spain
2. Barbieri C, Mari F, Stopper A, Gatti E, Escandell-Montero P, Martínez-Martínez JM, Martín-Guerrero JD (2015) A new machine learning approach for predicting the response to anemia treatment in a large cohort of end stage renal disease patients undergoing dialysis. Comput Biol Med 61:56–61
3. Barbieri C, Bolzoni E, Mari F, Cattinelli I, Bellocchio F, Martin JD, Amato C, Stopper A, Gatti E, Macdougall IC, Stuard S, Canaud B (2016a) Performance of a predictive model for long-term hemoglobin response to darbepoetin and iron administration in a large cohort of hemodialysis patients. PLOS ONE 11:1–18
4. Barbieri C, Molina M, Ponce P, Tothova M, Cattinelli I, Titapiccolo JI, Mari F, Amato C, Leipold F, Wehmeyer W et al (2016b) An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. Kidney Int 90(2):422–429
5. Berns JS, Elzein H, Lynn RI, Fishbane S, Meisels IS, Deoreo PB (2003) Hemoglobin variability in epoetin-treated hemodialysis patients. Kidney Int 64(4): 1514–21