Development of machine learning algorithms for prediction of mortality in spinal epidural abscess.

Journal: The spine journal : official journal of the North American Spine Society
Published Date: Dec 1, 2019

Abstract

BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements. Forecasting this potentially avoidable consequence at the time of admission could improve patient management and counseling. Few studies exist to meet this need, and none have explored methodologies such as machine learning.

Authors

  • Aditya V Karhade
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Akash A Shah
    David Geffen School of Medicine UCLA, Los Angeles, CA, USA.
  • Christopher M Bono
    1Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Marco L Ferrone
    Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
  • Sandra B Nelson
    Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.
  • Andrew J Schoenfeld
    Department of Orthopedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Mitchel B Harris
    Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, USA.
  • Joseph H Schwab
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: jhschwab@mgh.harvard.edu.

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