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Machine Learning to Identify Dialysis Patients at High Death Risk.
Akbilgic, Oguz; Obi, Yoshitsugu; Potukuchi, Praveen K; Karabayir, Ibrahim; Nguyen, Danh V; Soohoo, Melissa; Streja, Elani; Molnar, Miklos Z; Rhee, Connie M; Kalantar-Zadeh, Kamyar; Kovesdy, Csaba P.
Afiliação
  • Akbilgic O; Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
  • Obi Y; Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, Illinois, USA.
  • Potukuchi PK; Harold Simmons Center for Kidney Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California Irvine Medical Center, Orange, California, USA.
  • Karabayir I; Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
  • Nguyen DV; Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
  • Soohoo M; Faculty of Economics and Administrative Sciences, Kirklareli University, Kirklareli, Turkey.
  • Streja E; Division of General Internal Medicine, University of California Irvine Medical Center, Orange, California, USA.
  • Molnar MZ; Harold Simmons Center for Kidney Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California Irvine Medical Center, Orange, California, USA.
  • Rhee CM; Harold Simmons Center for Kidney Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California Irvine Medical Center, Orange, California, USA.
  • Kalantar-Zadeh K; Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
  • Kovesdy CP; Department of Surgery, Methodist University Hospital Transplant Institute, Memphis, Tennessee, USA.
Kidney Int Rep ; 4(9): 1219-1229, 2019 Sep.
Article em En | MEDLINE | ID: mdl-31517141
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Kidney Int Rep Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Kidney Int Rep Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos