Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.

Journal: Neurosurgery
Published Date: Mar 15, 2021

Abstract

BACKGROUND: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructured text.

Authors

  • Whitney E Muhlestein
    Department of Neurosurgery, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
  • Meredith A Monsour
    Vanderbilt University School of Medicine, Nashville, Tennessee.
  • Gabriel N Friedman
    Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Aniket Zinzuwadia
    Harvard Medical School, Boston, Massachusetts, USA.
  • Marcus A Zachariah
    Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Jean-Valery Coumans
    Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Bob S Carter
    Department of Neurosurgery, University of California San Diego, La Jolla, CA, 92103, USA.
  • Lola B Chambless
    Department of Neurosurgery, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.

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