Predictive Capacities of a Machine Learning Decision Tree Model Created to Analyse Feasibility of an Open or Robotic Kidney Transplant.

Journal: The international journal of medical robotics + computer assisted surgery : MRCAS
PMID: 39716399

Abstract

BACKGROUND: Machine learning has emerged as a potent tool in healthcare. A decision tree model was built to improve the decision-making process when determining the optimal choice between an open or robotic surgical approach for kidney transplant.

Authors

  • Alessandro Martinino
    Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, Illinois, USA.
  • Ojus Khanolkar
    University of Illinois at Chicago College of Medicine, Chicago, Illinois, USA.
  • Erdem Koyuncu
    Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA.
  • Egor Petrochenkov
    Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, Illinois, USA.
  • Giulia Bencini
    Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, Illinois, USA.
  • Joanna Olazar
    Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, Illinois, USA.
  • Pierpaolo Di Cocco
  • Jorge Almario-Alvarez
    Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, Illinois, USA.
  • Mario Spaggiari
  • Enrico Benedetti
    Department of Surgery, University of Illinois at Chicago College of Medicine, Chicago, IL, USA.
  • Ivo Tzvetanov

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