Machine Learning With Feature Domains Elucidates Candidate Drivers of Hospital Readmission Following Spine Surgery in a Large Single-Center Patient Cohort.

Journal: Neurosurgery
Published Date: Sep 15, 2020

Abstract

BACKGROUND: Unplanned hospital readmissions constitute a significant cost burden in healthcare. Identifying factors contributing to readmission risk presents opportunities for actionable change to reduce readmission rates.

Authors

  • Michael L Martini
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Sean N Neifert
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Eric K Oermann
    Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Jonathan Gal
    Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Kanaka Rajan
    Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, United States of America.
  • Dominic A Nistal
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York.
  • John M Caridi
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY.

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