A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery.

Journal: Head & neck
Published Date: May 1, 2024

Abstract

BACKGROUND: New patient referrals are often processed by practice coordinators with little-to-no medical background. Treatment delays due to incorrect referral processing, however, have detrimental consequences. Identifying variables that are associated with a higher likelihood of surgical oncological resection may improve patient referral processing and expedite the time to treatment. The study objective is to develop a supervised machine learning (ML) platform that identifies relevant variables associated with head and neck surgical resection.

Authors

  • Max L Jiam
    School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
  • Kevin Z Xin
    Transitional Year Program, Mount Carmel Health System, Grove City, OH, USA.
  • Patrick K Ha
    Department of Otolaryngology - Head & Neck Surgery, University of California - San Francisco, San Francisco, California, USA.
  • Nicole T Jiam
    Department of Otolaryngology - Head & Neck Surgery, University of California - San Francisco, San Francisco, California, USA.

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