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

> **NIH NIH F31** · COLUMBIA UNIVERSITY TEACHERS COLLEGE · 2024 · $15,140

## Abstract

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.

## Key facts

- **NIH application ID:** 10831047
- **Project number:** 5F31MH127887-03
- **Recipient organization:** COLUMBIA UNIVERSITY TEACHERS COLLEGE
- **Principal Investigator:** ILANA GRATCH
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $15,140
- **Award type:** 5
- **Project period:** 2022-05-01 → 2024-06-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10831047

## Citation

> US National Institutes of Health, RePORTER application 10831047, Detecting suicide risk in adolescents and young adults: A machine learning-based analysis of nonverbal behaviors exhibited during suicide assessments (5F31MH127887-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10831047. Licensed CC0.

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