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

项目摘要 自杀是美国15 - 24岁人群的第二大死因。一个具有挑战性 预防自杀的一个组成部分是发现高危青年。先前的研究表明， 绝大多数自杀死者在最后一次与心理健康专家交谈时否认自杀意念。 提供商因此，只有15%的心理健康专业人员表示感到非常自信也就不足为奇了。 评估青少年自杀风险。行为标记为更客观地确定风险提供了一种途径。 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操作化 时间和任务性能，只是触及表面的什么是可能的丰富，动态的性质， 行为数据计算科学的最新进展提供了一个机会， 不易量化甚至不易被人类感知的信息。本研究旨在利用 基于机器学习的方法来描述自杀过程中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自杀的青少年和年轻人。 具体来说，我们将自动提取非语言特征，自发的面部动作，和头部 青少年和年轻人以及他们的临床访谈者所表现出的运动。我们将使用传统的 假设检验，以检查是否由先前的研究所告知的一组非语言行为 区分自杀（即，过去一年主动自杀意念）和非自杀（即，没有自杀史 思想/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，经实验确定的非语言行为都可能有助于我们识别自杀的能力。 参与者（目标2）。数据将从自我伤害思想的录音管理中提取， 行为访谈-修订有自杀和非自杀的青少年和年轻人（n = 232; 12 - 19岁）， 和录像管理的哥伦比亚自杀严重程度评定量表与自杀和 无自杀倾向的年轻成人（n = 70; 18 - 24岁）。着眼于未来预测自杀行为， 未来的研究，这条工作线的长期目标是利用计算方法来量化非 语言行为，可以用来检测自杀风险客观和规模。

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.