Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer.

Journal: JAMA otolaryngology-- head & neck surgery
Published Date:

Abstract

IMPORTANCE: Patient-reported symptom burden was recently found to be associated with emergency department use and unplanned hospitalization (ED/Hosp) in patients with head and neck cancer. It was hypothesized that symptom scores could be combined with administrative health data to accurately risk stratify patients.

Authors

  • Christopher W Noel
    Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
  • Rinku Sutradhar
    Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; ICES, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada. Electronic address: rinku.sutradhar@ices.on.ca.
  • Lesley Gotlib Conn
    Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • David Forner
    Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Wing C Chan
    ICES, Toronto, Ontario, Canada.
  • Rui Fu
    Institute of Health Policy, Management and Evaluation, University of Toronto, Health Sciences Building, 155 College Street, Suite 425, Toronto, Ontario, M5T 3M6, Canada.
  • Julie Hallet
    Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Natalie G Coburn
    Division of General Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada.
  • Antoine Eskander
    Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada.