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A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial
Author(s) -
Pestian John P.,
Sorter Michael,
Connolly Brian,
Bretonnel Cohen Kevin,
McCullumsmith Cheryl,
Gee Jeffry T.,
Morency LouisPhilippe,
Scherer Stefan,
Rohlfs Lesley
Publication year - 2017
Publication title -
suicide and life‐threatening behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.544
H-Index - 90
eISSN - 1943-278X
pISSN - 0363-0234
DOI - 10.1111/sltb.12312
Subject(s) - nonverbal communication , suicide prevention , psychology , clinical psychology , injury prevention , human factors and ergonomics , poison control , medicine , psychiatry , medical emergency , developmental psychology
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.