Citation

Venek V, Scherer S, Morency LP, Rizzo AS, Pestian J. IEEE Trans. Affect. Comput. 2017; 8(2): 204-215.

Copyright

(Copyright © 2017, IEEE Computer Society)

DOI

10.1109/TAFFC.2016.2518665

PMID

unavailable

Abstract

Youth suicide is a major public health problem. It is the third leading cause of death in the United States for ages 13 through 18. Many adolescents that face suicidal thoughts or make a suicide plan never seek professional care or help. Within this work, we evaluate both verbal and nonverbal responses to a five-item ubiquitous questionnaire to identify and assess suicidal risk of adolescents. We utilize a machine learning approach to identify suicidal from non-suicidal speech as well as characterize adolescents that repeatedly attempted suicide in the past. Our findings investigate both verbal and nonverbal behavior information of the face-to-face clinician-patient interaction. We investigate 60 audio-recorded dyadic clinician-patient interviews of 30 suicidal (13 repeaters and 17 non-repeaters) and 30 non-suicidal adolescents. The interaction between clinician and adolescents is statistically analyzed to reveal differences between suicidal versus non-suicidal adolescents and to investigate suicidal repeaters' behaviors in comparison to suicidal non-repeaters. By using a hierarchical classifier we were able to show that the verbal responses to the ubiquitous questions sections of the interviews were useful to discriminate suicidal and non-suicidal patients. However, to additionally classify suicidal repeaters and suicidal non-repeaters more information especially nonverbal information is required. © 2010-2012 IEEE.

Language: en

Keywords

Behavior analytics; clinician-patient interaction; hierarchical classifiers; ubiquitous questions; youth suicide