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

    1 Citation (Scopus)

    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

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