Survival analysis using machine learning in transplantation: a practical introduction.

Journal: BMC medical informatics and decision making
PMID: 40119422

Abstract

BACKGROUND: Survival analysis is a critical tool in transplantation studies. The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making. This study aims to provide an introduction to the application of the RSF model in survival analysis in kidney transplantation alongside a practical guide to develop and evaluate predictive algorithms.

Authors

  • Andrea Garcia-Lopez
    PhD Program in Clinical Epidemiology, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Pontificia Universidad Javeriana, Bogotá, Colombia.
  • Maritza Jiménez-Gómez
    Research Department, Colombiana de Trasplantes, Bogotá, Colombia.
  • Andrea Gomez-Montero
    Research Department, Colombiana de Trasplantes, Bogotá, Colombia.
  • Juan Camilo Gonzalez-Sierra
    Universidad de los Andes, Bogotá, Colombia.
  • Santiago Cabas
    Research Department, Colombiana de Trasplantes, Bogotá, Colombia.
  • Fernando Giron-Luque
    Department of Transplant Research, Colombiana de Trasplantes, Bogotá, Colombia.

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