Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis.

Journal: Journal of cancer survivorship : research and practice
PMID:

Abstract

PURPOSE: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim was to predict individual risks for developing CRF.

Authors

  • Lian Beenhakker
    Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands.
  • Kim A E Wijlens
    Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands.
  • Annemieke Witteveen
    Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands. a.witteveen@utwente.nl.
  • Marianne Heins
    Department of Primary Care, Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands.
  • Joke C Korevaar
    Department of Primary Care, Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands.
  • Kelly M de Ligt
    Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Christina Bode
    Department of Psychology, Health and Technology, University of Twente, Enschede, The Netherlands.
  • Miriam M R Vollenbroek-Hutten
    Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands.
  • Sabine Siesling
    Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands.