Machine Learning to Identify Dialysis Patients at High Death Risk
Oguz Akbilgic1,2, Yoshitsugu Obi3, Praveen K. Potukuchi4, Ibrahim Karabayir1,5, Danh V. Nguyen6, Melissa Soohoo3, Elani Streja3, Miklos Z. Molnar4,7,8,9, Connie M. Rhee3, Kamyar Kalantar-Zadeh3, Csaba P. Kovesdy4
1Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
2Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, Illinois, USA
3Harold Simmons Center for Kidney Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California Irvine Medical Center, Orange, California, USA
4Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
5Faculty of Economics and Administrative Sciences, Kirklareli University, Kirklareli, Turkey
6Division of General Internal Medicine, University of California Irvine Medical Center, Orange, California, USA
7Department of Surgery, Methodist University Hospital Transplant Institute, Memphis, Tennessee, USA
8Division of Transplant Surgery, Department of Surgery, University of Tennessee Health Science Center, Memphis, Tennessee, USA
9Department of Transplantation and Surgery, Semmelweis University, Budapest, Hungary
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Kidney International Reports
Tập 4
1219-1229
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