Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

Journal: BMC musculoskeletal disorders
PMID: 38773464

Abstract

BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility.

Authors

  • Mert Karabacak
    Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA.
  • Abhiraj D Bhimani
    Department of Neurosurgery, Mount Sinai Health System, 1468 Madison Ave, New York, NY, USA.
  • Alexander J Schupper
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Matthew T Carr
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Jeremy Steinberger
    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Orthopedic Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA. Electronic address: Jeremy.steinberger@mountsinai.org.
  • Konstantinos Margetis
    Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA.

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