Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers-Reference-Cited by-同舟云学术

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

Published:2018-06-13 Issue:7 Volume:45 Page:3449-3459
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Deist Timo M.¹²,Dankers Frank J. W. M.²³,Valdes Gilmer⁴,Wijsman Robin³,Hsu I‐Chow⁴,Oberije Cary²,Lustberg Tim⁵,Soest Johan⁵,Hoebers Frank⁵,Jochems Arthur¹²,El Naqa Issam⁶,Wee Leonard⁵,Morin Olivier⁴,Raleigh David R.⁴,Bots Wouter³⁷,Kaanders Johannes H.³,Belderbos José⁸,Kwint Margriet⁸,Solberg Timothy⁴,Monshouwer René³,Bussink Johan³,Dekker Andre⁵,Lambin Philippe¹

Affiliation:

1. The D‐lab: Decision Support for Precision Medicine GROW ‐ School for Oncology and Developmental Biology Maastricht University Medical Centre+ Universiteitssingel 40 6229 ER Maastricht The Netherlands

2. Department of Radiation Oncology GROW, School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht The Netherlands

3. Department of Radiation Oncology Radboud University Medical Center Nijmegen The Netherlands

4. Department of Radiation Oncology University of California San Francisco San Francisco CA USA

5. Department of Radiation Oncology (MAASTRO) GROW, School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht The Netherlands

6. Department of Radiation Oncology University of Michigan Ann Arbor Michigan USA

7. Institute for Hyperbaric Oxygen (IvHG) Arnhem The Netherlands

8. Department of Radiation Oncology The Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital Amsterdam The Netherlands

Funder

H2020 European Research Council

Stichting voor de Technische Wetenschappen

Ministerie van Economische Zaken

Seventh Framework Programme

H2020 Innovation In SMEs

KWF Kankerbestrijding

Kurt Weill Foundation for Music

Nanjing Forestry University

Varian Medical Systems

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.12967

Reference34 articles.

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4. Do we need hundreds of classifiers to solve real world classification problems?;Fernández‐Delgado M;J Mach Learn Res,2014

5. WainerJ.Comparison of 14 different families of classification algorithms on 115 binary datasets. ArXiv160600930 Cs. June2016.http://arxiv.org/abs/1606.00930. Accessed April 8 2017.

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