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。项目摘要/摘要 拟议的研究将利用主动和被动的门诊评估（AA）方法和机器 学会在青年临床样本中开发个性化的自杀想法（SI）预测模型 高危自杀。考虑到自杀目前是第二领先的，这项工作是及时且重要的 cause of death among youth in the U.S., and rates of SI and suicidal behavior have risen steadily over the past 20年。 SI是预测模型的关键目标，因为它是可识别，可靠且可修改的先行目标 自杀行为。开发可以有效预测SI的预测模型可能为即将到来的道路铺平道路 时间干预以峰值风险的精确时间提供，因此在发生自杀行为之前就可以防止自杀行为。 但是，尽管进行了数十年的研究，但我们准确预测SI的能力仍然很差，这可能是因为 自杀性是由于上下文化动态过程之间复杂的相互作用而产生的，而动态过程很大程度上是 特定于每个人。然而，大多数研究试图预测SI，这是一个非常特定于人的特定于人的 来自小组级数据的现象 - 可以充分地确定谁处于危险之中，但不能何时何时 有风险，这对于预防至关重要。为了提高我们对SI的理解和预测，这是必须的 采用适当说明个体可变性（即个性化或精确医学）的方法， 因此，模型适合该人，反之亦然。 门诊评估，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 型号。我建议将这些方法论进步结合起来，以开发SI的个性化模型 特别是，在高风险自杀的年轻人中，本研究将使用生态学 瞬时评估（EMA）每天两次评估SI严重性并收集连续的被动传感器数据 连续100天从智能手机进行。该项目将被动收集的传感器数据映射到 迫切且理论上与自杀风险相关的变量，例如体育锻炼和流动性， 沟通和社交互动。 SI严重性和连续被动的每日评级的组合 传感器数据将提供必要的数据来开发个性化的风险计算器，以建模每个人的 SI严重程度的可变性是被动传感器数据的函数。这项研究将进一步当前概念化 使用先进的方法论和计算方法的自杀风险和预测，并提供 具有创新方法的培训，有可能实时预测SI风险，这对 NIMH战略计划的目标2.2和4.1，并具有改善自杀的巨大希望 预防努力。