Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods

BACKGROUND: Predictive models for delayed graft function (DGF) after kidney transplantation are usually developed using logistic regression. We want to evaluate the value of machine learning methods in the prediction of DGF. METHODS: 497 kidney transplantations from deceased donors at the Ghent Univ...

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Detalles Bibliográficos
Autores principales: Decruyenaere, Alexander, Decruyenaere, Philippe, Peeters, Patrick, Vermassen, Frank, Dhaene, Tom, Couckuyt, Ivo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607098/
https://www.ncbi.nlm.nih.gov/pubmed/26466993
http://dx.doi.org/10.1186/s12911-015-0206-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4607098/
https://www.ncbi.nlm.nih.gov/pubmed/26466993
http://dx.doi.org/10.1186/s12911-015-0206-y

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