A machine learning approach for predictive models of adverse events following spine surgery.

Journal: The spine journal : official journal of the North American Spine Society
Published Date:

Abstract

BACKGROUND: Rates of adverse events following spine surgery vary widely by patient-, diagnosis-, and procedure-related factors. It is critical to understand the expected rates of complications and to be able to implement targeted efforts at limiting these events.

Authors

  • Summer S Han
    Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Tej D Azad
  • Paola A Suarez
    Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • John K Ratliff
    Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: jratliff@stanford.edu.