Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing.

Journal: Epilepsia
PMID: 31111463

Abstract

OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is an important cause of mortality in epilepsy. However, there is a gap in how often providers counsel patients about SUDEP. One potential solution is to electronically prompt clinicians to provide counseling via automated detection of risk factors in electronic medical records (EMRs). We evaluated (1) the feasibility and generalizability of using regular expressions to identify risk factors in EMRs and (2) barriers to generalizability.

Authors

  • Kristen Barbour
    Division of Child Neurology, Weill Cornell Medicine, New York, New York.
  • Dale C Hesdorffer
    Department of Epidemiology, Columbia University Medical Center, New York, New York.
  • Niu Tian
    Centers for Disease Control and Prevention, Atlanta, Georgia.
  • Elissa G Yozawitz
    Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York.
  • Patricia E McGoldrick
    Department of Neurology, Mount Sinai Health System, New York, New York.
  • Steven Wolf
    Department of Neurology, Mount Sinai Health System, New York, New York.
  • Tiffani L McDonough
    Department of Epidemiology, Columbia University Medical Center, New York, New York.
  • Aaron Nelson
    Department of Neurology, New York University Langone Medical Center, New York, New York.
  • Tobias Loddenkemper
    Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA.
  • Natasha Basma
    Division of Child Neurology, Weill Cornell Medicine, New York, New York.
  • Stephen B Johnson
    Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY.
  • Zachary M Grinspan
    Division of Child Neurology, Weill Cornell Medicine, New York, New York.

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