Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.

Journal: Evidence-based mental health
Published Date: Jul 24, 2017

Abstract

BACKGROUND: We report a study of machine learning applied to the phenotyping of psychiatric diagnosis for research recruitment in youth depression, conducted with 861 labelled electronic medical records (EMRs) documents. A model was built that could accurately identify individuals who were suitable candidates for a study on youth depression.

Authors

  • Joseph Geraci
    NetraMark Corp, Toronto, ON, Canada.
  • Pamela Wilansky
    Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
  • Vincenzo De Luca
    Group for Suicide Studies, Centre for Addiction and Mental Health, 250 College Street, Toronto, Ontario M5T 1R8, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. Electronic address: vincenzo_deluca@camh.net.
  • Anvesh Roy
    Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
  • James L Kennedy
    Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
  • John Strauss
    Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

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