Leveraging ambulatory assessment data and machine learning to develop personalized prediction models of suicidal ideation

利用动态评估数据和机器学习开发自杀意念的个性化预测模型

• 批准号: 10386055
• 负责人: Craig Jeffrey Robb Sewall
• 金额: $ 3.52万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2022-12-11
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10386055
• 关键词: Behavioral; Cause of Death; Cellular Phone; Clinical; Clinical assessments; Collection; Communication; Complex; Custom; Data; Data Analyses; Data Collection; Devices; Ecological momentary assessment; Emergency Situation; Feeling suicidal; Future; Head Start Program; Hour; Individual; Intervention; Link; Machine Learning; Maps; Methodology; Methods; Modeling; National Institute of Mental Health; Nature; Participant; Patient Self-Report; Pattern; Persons; Physical activity; Population; Prevention; Process; Research; Research Design; Resolution; Risk; Risk Factors; Sampling; Series; Severities; Sleep; Social Interaction; Strategic Planning; Suicide; Suicide attempt; Suicide prevention; Time; Training; Visit; Work; Youth; adaptive intervention; high risk; improved; individual variation; innovation; machine learning algorithm; mobile computing; personalized medicine; personalized predictions; personalized risk prediction; post-doctoral training; precision medicine; predictive modeling; prevent; real time monitoring; response; risk prediction; risk prediction model; sensor; social; suicidal behavior; suicidal risk; suicide rate; time use

A.7. PROJECT SUMMARY/ABSTRACT The proposed research will leverage active and passive ambulatory assessment (AA) methods and machine learning to develop personalized suicidal ideation (SI) prediction models among a clinical sample of youth at high-risk for suicidality. This work is timely and important, given that suicide is currently the second-leading cause of death among youth in the U.S., and rates of SI and suicidal behavior have risen steadily over the past 20 years. SI is a critical target for predictive models, as it is an identifiable, reliable, and modifiable antecedent of suicidal behavior. Developing predictive models that can effectively predict SI may pave the way for just-in- time interventions delivered at the precise time of peak risk, thus preventing suicidal behavior before it occurs. However, despite decades of research, our ability to accurately predict SI remains poor, likely because suicidality results from a complex interaction between contextualized dynamic processes that are largely specific to each individual. Yet, most research has attempted to predict SI, a highly person-specific phenomenon, from group-level data—which can adequately identify who is at risk but not when an individual is at risk, which is critical for prevention. Thus, to improve our understanding and prediction of SI it is imperative to take an approach that properly accounts for individual variability (i.e., personalized or precision medicine), whereby the model is fit to the person rather than vice versa. Advancements in ambulatory assessment, mobile computing, and machine learning allow for the collection, management, and analysis of dynamic, high-resolution data required to develop personalized risk prediction models. I propose to combine these methodological advancements to develop personalized models of SI prediction. Specifically, among a population of youth at high-risk for suicidality, this study will use ecological momentary assessment (EMA) to assess SI severity twice daily and collect continuous passive sensor data from smartphones for 100 consecutive days. This project will map passively collected sensor data onto variables that are empirically and theoretically linked to suicide risk, such as physical activity and mobility, communication, and social interaction. This combination of daily ratings of SI severity and continuous passive sensor data will provide the necessary data to develop personalized risk calculators that model each person’s variability in SI severity as a function of passive sensor data. This study will further current conceptualizations of suicide risk and prediction using advanced methodological and computational approaches, and provide training in innovative methods that have the potential to predict SI risk in real-time, which is responsive to Objectives 2.2 and 4.1 of NIMH’s Strategic Plan and holds tremendous promise for improving suicide prevention efforts.

A.7. 项目概要/摘要 拟议的研究将利用主动和被动动态评估（AA）方法和机器 学习在青年临床样本中开发个性化自杀意念 (SI) 预测模型 鉴于自杀目前是第二大风险，这项工作是及时且重要的。 美国青少年的死因，SI 和自杀行为的发生率在过去稳步上升 SI 是预测模型的关键目标，因为它是可识别、可靠且可修改的前提。 开发能够有效预测 SI 的预测模型可能为即时自杀行为铺平道路。 在风险高峰的精确时间进行时间干预，从而在自杀行为发生之前预防自杀行为。 然而，尽管经过数十年的研究，我们准确预测 SI 的能力仍然很差，可能是因为 自杀是情境化动态过程之间复杂相互作用的结果，这些过程很大程度上是 然而，大多数研究尝试高度预测 SI，这是一个因人而异的现象。 现象，来自群体层面的数据，可以充分识别谁处于危险之中，但不能识别个人何时处于危险之中 因此，提高我们对 SI 的理解和预测势在必行。 采取适当考虑个体差异的方法（即个性化或精准医疗）， 因此，模型适合人，而不是相反。 动态评估、移动计算和机器学习的进步允许收集、 管理和分析开发个性化风险预测所需的动态高分辨率数据 我建议结合这些方法论的进步来开发 SI 的个性化模型。 具体来说，在自杀高风险的青少年群体中，本研究将使用生态学方法。 瞬时评估 (EMA)，每天两次评估 SI 严重程度并收集连续的无源传感器数据 该项目将连续 100 天从智能手机收集被动收集的传感器数据。 在经验和理论上与自杀风险相关的变量，例如体力活动和活动能力， SI 严重程度的每日评级和持续被动的结合。 传感器数据将提供必要的数据来开发个性化风险计算器，对每个人的风险进行建模 SI 严重程度随无源传感器数据变化的研究将进一步深化当前的概念。 使用先进的方法和计算方法进行自杀风险和预测，并提供 创新方法的培训有可能实时预测 SI 风险，该方法对 NIMH 战略计划的目标 2.2 和 4.1 为改善自杀带来了巨大希望 预防工作。