Citation

Pestian JP, Sorter M, Connolly B, Bretonnel Cohen K, McCullumsmith C, Gee JT, Morency LP, Scherer S, Rohlfs L. Suicide Life Threat. Behav. 2016; 47(1): 112-121.

Affiliation

Institute for Creative Technologies, University of Southern California, Los Angeles, CA, USA.

Copyright

(Copyright © 2016, American Association of Suicidology, Publisher John Wiley and Sons)

DOI

10.1111/sltb.12312

PMID

27813129

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.

© 2016 The American Association of Suicidology.

Language: en