PROJECT SUMMARY Suicide is the second leading cause of death among 15-24-year-olds in the United States. A challenging component of suicide prevention is the detection of high-risk young people. Prior research suggests that the vast majority of suicide decedents deny suicidal ideation in their last conversation with a mental health provider. It is thus unsurprising that only 15% of mental health professionals report feeling very confident assessing youth suicide risk. Behavioral markers offer one avenue for more objective risk determination. Despite progress in this area, behavioral markers have been operationalized primarily in the form of reaction times and task performance, only scratching the surface of what is possible with the rich, dynamic nature of behavioral data. Recent advances in computational science offer an opportunity to model behavioral information that is not easily quantifiable or even perceivable to human beings. This study aims to employ machine learning-based approaches to characterize non-verbal behaviors exhibited during suicide assessments, and test whether these behaviors can be used to identify suicidal adolescents and young adults. Specifically, we will automatically extract paralinguistic characteristics, spontaneous facial action, and head motion exhibited by adolescents and young adults, and their clinical interviewers. We will use traditional hypothesis testing to examine whether a set of non-verbal behaviors informed by previous research differentiate suicidal (i.e., past year active suicidal ideation) and nonsuicidal (i.e., no lifetime history of suicidal thoughts/behaviors) adolescents and young adults (Aim 1). We will then use machine learning to test whether any additional, empirically-determined non-verbal behaviors may contribute to our ability to identify suicidal participants (Aim 2). Data will be drawn from audio-recorded administrations of the Self-Injurious Thoughts and Behaviors Interview-Revised with suicidal and nonsuicidal adolescents and young adults (n=232; 12-19 yrs), and video-recorded administrations of the Columbia-Suicide Severity Rating Scale with suicidal and nonsuicidal young adults (n=70; 18-24 yrs). With an eye toward prospective prediction of suicidal behavior in future research, the long-term goal of this line of work is to harness computational methods to quantify non- verbal behaviors that can be used to detect suicide risk objectively and at scale.

PROJECT NARRATIVE Suicide is the second leading cause of death in adolescents and young adults, and identifying high-risk young people remains an immense challenge. The proposed research will employ machine learning-based approaches to test whether non-verbal behaviors (i.e., paralinguistic characteristics, spontaneous facial action, and head motion) exhibited during suicide assessments can be used to identify adolescents and young adults with recent suicidal ideation. This project may inform the development of an objective method of detecting suicide risk at scale.