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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| 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 |