In Kidney international ; h5-index 87.0
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.
Coorey Craig Peter, Sharma Ankit, Mueller Samuel, Yang Jean
kidney, machine learning, supervised, transplantation, unsupervised