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Detecting suicide risk in adolescents and young adults: A machine learning-based analysis of nonverbal behaviors exhibited during suicide assessments

Award Number: F31MH127887
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

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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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $40,035 )
2024	2024	TEACHERS COLLEGE COLUMBIA UNIVERSITY	525 W 120TH ST	NEW YORK	NY	10027	NEW YORK	USA	Mental Health Research Grants	000	3	4/22/2024	NON-COMPETING CONTINUATION	$40,035
														Subtotal = $40,035

Issue Date FY: 2023 ( Subtotal = $39,189 )
2023	2023	TEACHERS COLLEGE, COLUMBIA UNIVERSITY	525 W 120TH ST	NEW YORK	NY	10027	NEW YORK	USA	Mental Health Research Grants	000	2	4/20/2023	NON-COMPETING CONTINUATION	$39,189
														Subtotal = $39,189

Issue Date FY: 2022 ( Subtotal = $32,747 )
2022	2022	TEACHERS COLLEGE, COLUMBIA UNIVERSITY	525 W 120TH ST	NEW YORK	NY	10027	NEW YORK	USA	Mental Health Research Grants	000	1	4/20/2022	NEW	$32,747
2022	2022	TEACHERS COLLEGE, COLUMBIA UNIVERSITY	525 W 120TH ST	NEW YORK	NY	10027	NEW YORK	USA	Mental Health Research Grants	001	1	4/20/2022	NEW	$0
														Subtotal = $32,747

Grand Total All Awards = $111,971

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Detecting suicide risk in adolescents and young adults: A machine learning-based analysis of nonverbal behaviors exhibited during suicide assessments

Award Number: F31MH127887

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

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