Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boo...

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Detalles Bibliográficos
Autores principales: Deist, Timo M., Dankers, Frank J. W. M., Valdes, Gilmer, Wijsman, Robin, Hsu, I‐Chow, Oberije, Cary, Lustberg, Tim, van Soest, Johan, Hoebers, Frank, Jochems, Arthur, El Naqa, Issam, Wee, Leonard, Morin, Olivier, Raleigh, David R., Bots, Wouter, Kaanders, Johannes H., Belderbos, José, Kwint, Margriet, Solberg, Timothy, Monshouwer, René, Bussink, Johan, Dekker, Andre, Lambin, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
BIOLOGICAL PHYSICS AND RESPONSE PREDICTION
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6095141/
https://www.ncbi.nlm.nih.gov/pubmed/29763967
http://dx.doi.org/10.1002/mp.12967

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6095141/
https://www.ncbi.nlm.nih.gov/pubmed/29763967
http://dx.doi.org/10.1002/mp.12967

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