Using machine learning and an ensemble of methods to predict kidney transplant survival-Reference-Cited by-同舟云学术

Using machine learning and an ensemble of methods to predict kidney transplant survival

Published:2019-01-09 Issue:1 Volume:14 Page:e0209068
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Mark Ethan^ORCID,Goldsman David,Gurbaxani Brian,Keskinocak Pinar^ORCID,Sokol Joel

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference30 articles.

1. External validation of the estimated posttransplant survival score for allocation of deceased donor kidneys in the United States;PA Clayton;Am J Transplant,2014

2. A guide to calculating and interpreting the Estimated Post-Transplant Survival (EPTS) score used in the Kidney Allocation System (KAS). 2014. Available from: https://optn.transplant.hrsa.gov/media/1511/guide_to_calculating_interpreting_epts.pdf.

3. A recipient risk score for deceased donor renal allocation;ES Baskin-Bey;Am J Kidney Dis,2007

4. Predictability of survival models for waiting list and transplant patients: calculating LYFT;RA Wolfe;Am J Transplant,2009

5. Regression models and life-tables;DR Cox;J R Stat Soc Series B Stat Methodol,1972

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