Unsupervised learning to identify symptom clusters in older adults undergoing chemotherapy.

Journal: Journal of geriatric oncology
PMID: 40088865

Abstract

INTRODUCTION: Unsupervised machine learning (ML) approaches such as clustering have not been commonly applied to patient-reported data. This study describes ML methods to explore and describe patient-reported symptom trajectories in older adults receiving chemotherapy.

Authors

  • Erika Ramsdale
    James P. Wilmot Cancer Center, University of Rochester Medical Center, NY, USA. Electronic address: erika_ramsdale@urmc.rochester.edu.
  • Yilin Zhou
    Center for Integrated Research Computing, University of Rochester, Rochester, New York 14627, United States.
  • Lisa Smith
    James P. Wilmot Cancer Center, University of Rochester Medical Center, NY, USA.
  • Huiwen Xu
    James P. Wilmot Cancer Center, University of Rochester Medical Center, NY, USA; Department of Surgery, Cancer Control, University of Rochester Medical Center, NY, USA.
  • Rachael Tylock
    James P. Wilmot Cancer Center, University of Rochester Medical Center, NY, USA.
  • Marie Flannery
    School of Nursing, University of Rochester Medical Center, Rochester, New York.
  • Supriya Mohile
    James P. Wilmot Cancer Institute, Division of Hematology/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York.
  • Ajay Anand

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