Predicting graft survival in paediatric kidney transplant recipients using machine learning.

Journal: Pediatric nephrology (Berlin, Germany)
PMID: 39150523

Abstract

BACKGROUND: Identification of factors that affect graft survival in kidney transplantation can increase graft survival and reduce mortality. Artificial intelligence modelling enables impartial evaluation of clinician bias. This study aimed to examine factors that affect the survival of grafts in paediatric kidney transplantation through the use of machine learning.

Authors

  • Gülşah Kaya Aksoy
    Department of Pediatric Nephrology, Faculty of Medicine, Akdeniz University, Antalya, Turkey. gkayaaksoy@gmail.com.
  • Hüseyin Gökhan Akçay
    Department of Computer Engineering, Faculty of Engineering, Akdeniz University, Antalya, Turkey.
  • Çağlar Arı
    Department of Industrial Engineering, Faculty of Engineering, Koç University, Istanbul, Turkey.
  • Mehtap Adar
    Department of Pediatric Nephrology, Faculty of Medicine, Akdeniz University, Antalya, Turkey.
  • Mustafa Koyun
    Department of Pediatric Nephrology, Faculty of Medicine, Akdeniz University, Antalya, Turkey.
  • Elif Çomak
    Department of Pediatric Nephrology, Faculty of Medicine, Akdeniz University, Antalya, Turkey.
  • Sema Akman
    Department of Pediatric Nephrology, Faculty of Medicine, Akdeniz University, Antalya, Turkey.

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