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eJHaem
(May 2021)
Using machine learning to predict rapid decline of kidney function in sickle cell anemia
Fatma Güntürkün,
Daiqing Chen,
Oguz Akbilgic,
Robert L. Davis,
Ibrahim Karabayir,
Maxwell Strome,
Yang Dai,
Santosh L. Saraf,
Kenneth I. Ataga
Affiliations
Fatma Güntürkün
Center for Biomedical Informatics University of Tennessee Health Science Center Memphis USA
Daiqing Chen
Department of Bioengineering University of Illinois at Chicago Chicago USA
Oguz Akbilgic
Department of Health Informatics and Data Science Loyola University Chicago Maywood USA
Robert L. Davis
Center for Biomedical Informatics University of Tennessee Health Science Center Memphis USA
Ibrahim Karabayir
Department of Health Informatics and Data Science Loyola University Chicago Maywood USA
Maxwell Strome
Department of Computer Science University of Michigan Ann Arbor USA
Yang Dai
Department of Bioengineering University of Illinois at Chicago Chicago USA
Santosh L. Saraf
Department of Medicine University of Illinois at Chicago Chicago USA
Kenneth I. Ataga
Center for Sickle Cell Disease University of Tennessee Health Science Center Memphis USA
DOI
https://doi.org/10.1002/jha2.168
Journal volume & issue
Vol. 2, no. 2
pp. 257 – 260
Abstract
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No abstracts available.
Keywords
chronic kidney disease
machine learning models
predictive capacity
rapid decline of eGFR
sickle cell disease
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