Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their...

Descripción completa

Detalles Bibliográficos
Autores principales: Moncada-Torres, Arturo, van Maaren, Marissa C., Hendriks, Mathijs P., Siesling, Sabine, Geleijnse, Gijs
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7998037/
https://www.ncbi.nlm.nih.gov/pubmed/33772109
http://dx.doi.org/10.1038/s41598-021-86327-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7998037/
https://www.ncbi.nlm.nih.gov/pubmed/33772109
http://dx.doi.org/10.1038/s41598-021-86327-7

Ejemplares similares