Machine learning models to predict length of stay and discharge destination in complex head and neck surgery.

Journal: Head & neck
Published Date: Nov 3, 2020

Abstract

BACKGROUND: This study develops machine learning (ML) algorithms that use preoperative-only features to predict discharge-to-nonhome-facility (DNHF) and length-of-stay (LOS) following complex head and neck surgeries.

Authors

  • Khodayar Goshtasbi
    Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
  • Tyler M Yasaka
    Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
  • Mehdi Zandi-Toghani
    Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
  • Hamid R Djalilian
    Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
  • William B Armstrong
    Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
  • Tjoson Tjoa
    Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
  • Yarah M Haidar
    Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
  • Mehdi Abouzari
    Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.

Keywords

External Resources

Popular Topics
Recent Journals