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

We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell’s concordance index, than the Estimated Post Transplant Survival model used in the k...

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Detalles Bibliográficos
Autores principales: Mark, Ethan, Goldsman, David, Gurbaxani, Brian, Keskinocak, Pinar, Sokol, Joel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326487/
https://www.ncbi.nlm.nih.gov/pubmed/30625130
http://dx.doi.org/10.1371/journal.pone.0209068

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326487/
https://www.ncbi.nlm.nih.gov/pubmed/30625130
http://dx.doi.org/10.1371/journal.pone.0209068

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