Identifying epilepsy psychiatric comorbidities with machine learning.

Journal: Acta neurologica Scandinavica
PMID: 31889296

Abstract

OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine-learning classifiers that identify current or lifetime history of comorbid psychiatric conditions in teenagers and young adults with epilepsy.

Authors

  • Tracy Glauser
    Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
  • Daniel Santel
    Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 7024, Cincinnati, OH, 45229-3039, USA.
  • Melissa DelBello
    University of Cincinnati, Cincinnati, USA.
  • Robert Faist
    Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
  • Tonia Toon
    Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
  • Peggy Clark
    Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
  • Rachel McCourt
    Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
  • Benjamin Wissel
    Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
  • John Pestian
    Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., MLC 7024, Cincinnati, OH, 45229-3039, USA. john.pestian@cchmc.org.

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