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

检测青少年和年轻人的自杀风险：基于机器学习的自杀评估期间表现出的非语言行为分析

• 批准号: 10462337
• 负责人: ILANA GRATCH
• 金额: $ 3.27万
• 依托单位: COLUMBIA UNIVERSITY TEACHERS COLLEGE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10462337
• 关键词: Adolescent and Young Adult; Area; Behavior; Behavioral; Behavioral Model; Cause of Death; Characteristics; Clinical; Computational Science; Computing Methodologies; Contracts; Data; Detection; Development; Disease; Exhibits; Eye; Face; Feeling; Feeling suicidal; Goals; Head; Head Movements; Health Personnel; Health Professional; Human; Interview; Interviewer; Lateral; Machine Learning; Manuals; Mental Health; Methods; Motion; Movement; Muscle; Nature; Participant; Patients; Persons; Process; Radial; Reaction Time; Recording of previous events; Reporting; Research; Research Personnel; Risk; Self Administration; Severities; Smiling; Speech; Suggestion; Suicide; Suicide prevention; Surface; Task Performances; Testing; Thinking; Training; United States; Untranslated RNA; Validation; Work; Youth; base; experience; high risk; indexing; inferential statistics; prospective; response; suicidal; suicidal adolescent; suicidal behavior; suicidal individual; suicidal risk; support vector machine; young adult

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.

项目概要 自杀是美国 15-24 岁人群的第二大死因。一个具有挑战性的 预防自杀的一个组成部分是发现高危年轻人。先前的研究表明 绝大多数自杀者在最后一次与心理健康人士的谈话中否认有自杀意念 提供者。因此，只有 15% 的心理健康专业人士表示感到非常自信也就不足为奇了 评估青少年自杀风险。行为标记为更客观的风险确定提供了一种途径。 尽管在这一领域取得了进展，但行为标记主要以反应的形式进行操作 时间和任务绩效，只是触及了可能的表面，具有丰富的、动态的性质 行为数据。计算科学的最新进展为行为建模提供了机会 人类不易量化甚至感知的信息。本研究旨在雇用 基于机器学习的方法来表征自杀期间表现出的非语言行为 评估，并测试这些行为是否可以用来识别有自杀倾向的青少年和年轻人。 具体来说，我们将自动提取副语言特征、自发的面部动作和头部 青少年和年轻人及其临床访谈者表现出的动作。我们将使用传统的 假设检验，检查一系列非语言行为是否受到先前研究的影响 区分自杀性（即过去一年有主动自杀意念）和非自杀性（即一生中没有自杀史） 想法/行为）青少年和年轻人（目标 1）。然后我们将使用机器学习来测试是否 任何额外的、凭经验确定的非语言行为都可能有助于我们识别自杀倾向的能力 参与者（目标 2）。数据将从自伤思想和自我伤害的录音管理中提取 行为访谈——针对有自杀倾向和非自杀倾向的青少年和年轻人（n=232；12-19 岁）进行修订， 以及哥伦比亚自杀严重程度评定量表的录像管理，其中包括自杀和 无自杀倾向的年轻人（n=70；18-24 岁）。着眼于对自杀行为的前瞻性预测 在未来的研究中，这一工作的长期目标是利用计算方法来量化非 可用于客观、大规模地检测自杀风险的言语行为。