TY  - JOUR
PY  - 2017//
TI  - Adolescent suicidal risk assessment in clinician-patient interaction
JO  - IEEE transactions on affective computing
A1  - Venek, V.
A1  - Scherer, S.
A1  - Morency, L.-p.
A1  - Rizzo, A.S.
A1  - Pestian, J.
SP  - 204
EP  - 215
VL  - 8
IS  - 2
N2  - 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.<p /> <p>Language: en</p>
LA  - en
SN  - 2371-9850
UR  - http://dx.doi.org/10.1109/TAFFC.2016.2518665
ID  - ref1
ER  -