Prediction modeling-part 2: using machine learning strategies to improve transplantation outcomes.

Journal: Kidney international
Published Date: Sep 8, 2020

Abstract

Kidney transplant recipients and transplant physicians face important clinical questions where machine learning methods may help improve the decision-making process. This mini-review explores potential applications of machine learning methods to key stages of a kidney transplant recipient's journey, from initial waitlisting and donor selection, to personalization of immunosuppression and prediction of post-transplantation events. Both unsupervised and supervised machine learning methods are presented, including k-means clustering, principal components analysis, k-nearest neighbors, and random forests. The various challenges of these approaches are also discussed.

Authors

  • Craig Peter Coorey
    Centre for Kidney Research, Children's Hospital at Westmead, Westmead, New South Wales, Australia; Liverpool Hospital, South Western Sydney Clinical School, University of New South Wales and Western Sydney University, Sydney, New South Wales, Australia. Electronic address: c.coorey@unsw.edu.au.
  • Ankit Sharma
    Proteomics and Coagulation Unit, Thrombosis Research Institute, Bangalore, Karnataka 560099, India.
  • Samuel Muller
    School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia; Department of Mathematics and Statistics, Macquarie University, New South Wales, Australia.
  • Jean Yee Hwa Yang
    Centre for Mathematical Biology, School of Mathematics and Statistics, University of Sydney, Sydney, Australia.

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