Comparison of machine learning models for seizure prediction in hospitalized patients.

Journal: Annals of clinical and translational neurology
Published Date: Jun 27, 2019

Abstract

OBJECTIVE: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify low-risk patients (<5% seizures risk in 48 h).

Authors

  • Aaron F Struck
    Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
  • Andres A Rodriguez-Ruiz
    Department of Neurology, Emory University, Atlanta, Georgia.
  • Gamaledin Osman
    Department of Neurology, Henry Ford Hospital, Detroit, Michigan.
  • Emily J Gilmore
    Department of Neurology, Yale University, New Haven, Connecticut.
  • Hiba A Haider
    Department of Neurology, Emory University, Atlanta, Georgia.
  • Monica B Dhakar
    Department of Neurology, Emory University, Atlanta, Georgia.
  • Matthew Schrettner
    Department of Neurology, University of South Carolina Greenville, Greenville, South Carolina.
  • Jong W Lee
    Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Nicolas Gaspard
    Department of Neurology, Yale University, New Haven, Connecticut.
  • Lawrence J Hirsch
    Department of Neurology, Yale University, New Haven, Connecticut.
  • M Brandon Westover
    Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.

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