A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial.

Journal: Suicide & life-threatening behavior
Published Date: Feb 1, 2017

Abstract

Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects' words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.

Authors

  • John P Pestian
    Department of Pediatrics, Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center and Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA.
  • Michael Sorter
    Division of Psychiatry, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
  • Brian Connolly
    Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
  • Kevin Bretonnel Cohen
    Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.
  • Cheryl McCullumsmith
    Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
  • Jeffry T Gee
    Princeton Community Hospital, Princeton, WV, USA.
  • Louis-Philippe Morency
    Carnegie Mellon University, Pittsburgh, PA, USA.
  • Stefan Scherer
    Institute for Creative Technologies, University of Southern California, Los Angeles, CA, USA.
  • Lesley Rohlfs
    Institute for Creative Technologies, University of Southern California, Los Angeles, CA, USA.

Keywords

External Resources

Popular Topics
Recent Journals