A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial |
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| Authors: | John P. Pestian PhD Michael Sorter MD Brian Connolly PhD Kevin Bretonnel Cohen PhD Cheryl McCullumsmith MD PhD Jeffry T. Gee MD Louis‐Philippe Morency PhD Stefan Scherer PhD Lesley Rohlfs MS the STM Research Group |
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| Affiliation: | 1. Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA;2. Division of Psychiatry, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA;3. Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA;4. Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, OH, USA;5. Princeton Community Hospital, Princeton, WV, USA;6. Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;7. Institute for Creative Technologies, University of Southern California, Los Angeles, CA, USA |
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| 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. |
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