Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach.

Journal: World neurosurgery
PMID: 33744425

Abstract

BACKGROUND: Readmission after spine surgery is costly and a relatively common occurrence. Previous research identified several risk factors for readmission; however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspective in analysis of risk factors for readmission and can help predict the likelihood of this occurrence. This study evaluated a neural network (NN), a supervised machine learning technique, to determine whether it could predict readmission after 3 lumbar fusion procedures.

Authors

  • Eren O Kuris
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Ashwin Veeramani
    Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA.
  • Christopher L McDonald
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Kevin J DiSilvestro
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Andrew S Zhang
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Eric M Cohen
    Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, USA.
  • Alan H Daniels
    1Division of Spine Surgery and.

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