arXiv Machine Learning By Misaki Matsuura, Mohammadreza Nemati, Dulat Bekbolsynov, Stanislaw Stepkowski, Kevin S. Xu

Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants

Read the original on arXiv Machine Learning →

arXiv:2608. 03017v1 Announce Type: new Abstract: There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graft inevitably fails.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants

There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graft inevitably fails. These prediction algorithms could possibly be used for pre-transplant donor-recipient matching to identify more compatible donors and recipients and thus improve post-transplant outcomes.

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