Predicting kidney allograft survival with explainable machine learning.

Journal: Transplant immunology
PMID: 38797338

Abstract

INTRODUCTION: Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning method to identify these variables which may predict the early graft loss in kidney transplant patients and to assess their usefulness for improving clinical decisions.

Authors

  • Raquel A Fabreti-Oliveira
    Artificial Intelligence Laboratory, Departament of Computer Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Faculty of Medical Sciences of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; IMUNOLAB - Laboratory of Histocompatibility, Belo Horizonte, Minas Gerais, Brazil. Electronic address: raquel.fabreti@cienciasmedicasmg.edu.br.
  • Evaldo Nascimento
    IMUNOLAB - Laboratory of Histocompatibility, Belo Horizonte, Minas Gerais, Brazil; Faculty of Hospital Santa Casa, Belo Horizonte, Minas Gerais, Brazil. Electronic address: evaldo@imunolab.com.br.
  • Luiz Henrique de Melo Santos
    Artificial Intelligence Laboratory, Departament of Computer Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
  • Marina Ribeiro de Oliveira Santos
    University Hospital of Medical Sciences of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
  • Adriano Alonso Veloso
    Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.

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