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

Craig Peter Coorey, Ankit Sharma, Samuel Muller, Jean Yee Hwa Yang

Research output: Contribution to journalReview articlepeer-review

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.
Original languageEnglish
Pages (from-to)817-823
Number of pages7
JournalKidney International
Volume99
Issue number4
Early online date8 Sep 2020
DOIs
Publication statusPublished - Apr 2021

Keywords

  • kidney
  • machine learning
  • supervised
  • transplantation
  • unsupervised

Fingerprint Dive into the research topics of 'Prediction modeling-part 2: using machine learning strategies to improve transplantation outcomes'. Together they form a unique fingerprint.

Cite this