Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment.

Journal: Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PMID: 38359380

Abstract

PURPOSE: For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diagnosis. We evaluated whether a prognostic machine learning system could promote early PC, given existing capacity.

Authors

  • Jiang Chen He
    Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
  • Gordon Taylor Moffat
    Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
  • Sho Podolsky
    ICES, Toronto, ON, Canada.
  • Ferhana Khan
    ICES, Toronto, ON, Canada.
  • Ning Liu
    School of Public Health, Hangzhou Normal University, Hangzhou, China.
  • Nathan Taback
    Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada.
  • Steven Gallinger
    Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
  • Breffni Hannon
    Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
  • Monika K Krzyzanowska
    Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
  • Marzyeh Ghassemi
    Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Kelvin K W Chan
    ICES, Toronto, ON, Canada.
  • Robert C Grant
    Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.

Keywords

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