Author: Coorey, Craig Peter; Sharma, Ankit; Mueller, Samuel; Yang, Jean
Title: Prediction modelling - Part 2 - Using machine learning strategies to improve transplantation outcomes. Cord-id: rguo3rs5 Document date: 2020_9_8
ID: rguo3rs5
Snippet: 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 pres
Document: 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.
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