Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms.

Journal: World neurosurgery
PMID: 31634625

Abstract

BACKGROUND: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes.

Authors

  • Farrokh Farrokhi
    Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA.
  • Quinlan D Buchlak
    School of Medicine, The University of Notre Dame, Sydney, NSW, Australia. quinlan.buchlak1@my.nd.edu.au.
  • Matt Sikora
    Neuroscience Institute, Virginia Mason Medical Center, Seattle, Washington, USA.
  • Nazanin Esmaili
    School of Medicine, The University of Notre Dame, Sydney, NSW, Australia.
  • Maria Marsans
    Neuroscience Institute, Virginia Mason Medical Center, Seattle, Washington, USA.
  • Pamela McLeod
    Inland Neurosurgery and Spine Associates, Spokane, Washington, USA.
  • Jamie Mark
    Selkirk Neurology, Spokane, Washington, USA.
  • Emily Cox
    Providence Medical Research Center, Providence Health & Services, Spokane, Washington, USA.
  • Christine Bennett
    School of Medicine, The University of Notre Dame, Sydney, NSW, Australia.
  • Jonathan Carlson
    Inland Neurosurgery and Spine Associates, Spokane, Washington, USA.

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