Developing Personalized Predictive Models of Aggression

开发个性化的攻击性预测模型

• 批准号: 10662685
• 负责人: Colin Vize
• 金额: $ 18.02万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662685
• 关键词: Address; Adult; Affect; Age; Aggressive behavior; Anger; Behavior; Behavior Control; Behavior Disorders; Biological; Cellular Phone; Complex; Computing Methodologies; Data; Data Reporting; Detection; Development; Diagnosis; Diagnostic; Etiology; Family; Future; Goals; Heart Rate; Heterogeneity; Individual; Intervention; Life; Machine Learning; Measurement; Mental disorders; Mentors; Mentorship; Methodology; Methods; Modeling; Motivation; National Institute of Mental Health; Nature; Participant; Patient Self-Report; Pattern; Personality Traits; Persons; Phenotype; Physiological; Positioning Attribute; Prevention; Process; Protocols documentation; Psychological Models; Psychopathology; Public Health; Research; Research Design; Research Ethics; Resources; Risk; Risk Factors; Sampling; Scientific Inquiry; Scientist; Selection Criteria; Social Environment; Surveys; System; Technology; Testing; Time; Training; Universities; Youth; adaptive intervention; aggression prevention; analytical method; behavioral phenotyping; career; data streams; economic impact; effective therapy; emotion regulation; forest; gradient boosting; high risk population; improved; inclusion criteria; innovation; machine learning method; mobile sensing; novel; personalized intervention; personalized medicine; personalized predictions; predictive modeling; predictive tools; programs; prospective; psychologic; random forest; real time monitoring; recruit; research study; sensor; skills; smartphone based device; theories; wearable device; young adult

PROJECT SUMMARY/ABSTRACT Aggressive behavior is a transdiagnostic indicator of both youth and adult psychiatric disorders and a significant public health concern due to the direct harms to victims and its broader economic impact. Nonetheless, prediction of aggressive behavior is challenging due to significant variability in how, why, and when people act aggressively. This heterogeneity impedes efforts to establish etiological factors, identify biological substrates, and develop uniformly effective treatments. Though theories of aggression emphasize that it is a context-dependent, dynamic interpersonal behavior, research rarely attempts to study aggression in the contexts where it normally occurs and is most consequential (i.e., daily life). The current project seeks to improve on past research by studying the transdiagnostic mechanisms of aggression using novel analytic and measurement methodology necessary for pursuing a personalized medicine approach in aggressive behavior research and prevention. This project will use real-time data capture in conjunction with state-of-the-art analytic methods to deconstruct the heterogeneous behavioral phenotypes that relate to aggression. To achieve this, we will use relevant passively-sensed and self-reported data via smartphones from a sample of young adults (age=18-30; N=150) diagnosed with mental and behavioral disorders and at elevated risk for aggression. Data will be collected over the course of a 3-week ambulatory assessment protocol. We will apply machine learning methods capable of uncovering and modeling the complex dynamic processes observed in aggression at the level of each individual (i.e., personalized models) to prospectively predict aggressive urges and behavior. The results will pave the way for scalable just-in-time adaptive interventions tailored to an individual’s specific antecedents of aggression. The proposed study will contribute to NIMH Strategic Priorities 3.2 by 1) focusing on personalized models that can accommodate the complex topography of aggression and its antecedents and 2) applying innovative computational approaches (i.e., machine learning) to multiple streams of data (passively sensed, self-report) to identify potential just-in-time intervention targets for aggressive individuals. The comprehensive research and training plan detailed in this proposal will allow this candidate to address the primary research questions of the proposal and develop the expertise necessary to be an independent scientist. Specifically, this candidate will receive training in 1) personalized models of psychopathology and aggression; 2) methods for carrying out EMA-based studies and modeling intensive longitudinal data; and 3) collecting, processing, and predictive modeling with passive sensor data. This candidate has assembled a team of expert mentors (Wright, Jacobson) and consultants who possess the expertise to supervise the project and provide the training necessary to support the candidate in his development as an independent scientist. The expertise of the mentorship team, and the resources offered by the University of Pittsburgh, place the candidate in an ideal position to achieve his training, research, and career goals.

项目摘要/摘要 攻击性行为是青少年和成人精神障碍的跨诊断指标， 由于对受害者的直接伤害及其更广泛的经济影响，对公共卫生造成重大关切。 尽管如此，由于攻击性行为的方式、原因和方式存在显著的差异，因此对攻击性行为的预测具有挑战性 当人们表现得咄咄逼人的时候。这种异质性阻碍了确定病因、识别 生物基质，并开发统一有效的治疗方法。尽管攻击性理论强调 认为它是一种依赖于语境的、动态的人际行为，研究很少尝试在 它通常发生的和最重要的上下文(即日常生活)。目前的项目寻求 在过去研究的基础上，利用新的分析和分析方法研究攻击行为的跨诊断机制 在攻击性行为中追求个性化医学方法所需的测量方法 研究和预防。该项目将使用实时数据捕获和最先进的分析 方法解构与攻击性相关的异质行为表型。为了实现这一目标， 我们将通过智能手机使用来自年轻人样本的相关被动感知和自我报告的数据 (年龄18-30岁；N=150)被诊断为精神和行为障碍，攻击风险增加。数据 将在为期3周的门诊评估过程中收集。我们将应用机器学习 能够揭示和模拟在攻击中观察到的复杂动态过程的方法 每个人的水平(即，个性化的模型)，以前瞻性地预测攻击性冲动和行为。这个 结果将为可扩展的即时适应性干预铺平道路，这些干预措施根据个人的具体情况量身定做 侵略的前科。拟议的研究将有助于NIMH战略优先事项3.2通过1)重点 关于能够适应攻击及其前因和复杂地形的个性化模型 2)将创新的计算方法(即，机器学习)应用于多个数据流(被动 感知、自我报告)，以确定针对攻击性个人的潜在及时干预目标。这个 本提案中详细说明的全面研究和培训计划将使该候选人能够解决 提案的主要研究问题，并开发成为独立专家所需的专业知识 科学家。具体地说，这位候选人将接受1)个性化精神病理学模型和 攻击性；2)基于EMA的研究和密集纵向数据建模的方法；3) 使用被动传感器数据收集、处理和预测建模。这位候选人已经召集了一位 由专家导师(Wright、Jacobson)和顾问组成的团队，他们拥有监督项目的专业知识 并提供必要的培训，以支持候选人作为独立科学家的发展。 导师团队的专业知识，以及匹兹堡大学提供的资源，使 处于理想职位的候选人，能够实现他的培训、研究和职业目标。