Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis.
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| Title: | Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis. |
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| Authors: | Beenhakker L; Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands., Wijlens KAE; Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands., Witteveen A; Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands. a.witteveen@utwente.nl., Heins M; Department of Primary Care, Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands., Korevaar JC; Department of Primary Care, Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands., de Ligt KM; Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands., Bode C; Department of Psychology, Health and Technology, University of Twente, Enschede, The Netherlands., Vollenbroek-Hutten MMR; Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands.; Board of Directors, Medisch Spectrum Twente, Enschede, The Netherlands., Siesling S; Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands.; Department of Research and Development, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, The Netherlands. |
| Source: | Journal of cancer survivorship : research and practice [J Cancer Surviv] 2025 Apr; Vol. 19 (2), pp. 580-593. Date of Electronic Publication: 2023 Dec 07. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer Science + Business Media Country of Publication: United States NLM ID: 101307557 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1932-2267 (Electronic) Linking ISSN: 19322259 NLM ISO Abbreviation: J Cancer Surviv Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-2267 |
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| DOI: | 10.1007/s11764-023-01491-1 |
