Developing Personalized Predictive Models of Aggression

开发个性化的攻击性预测模型

基本信息

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
项目类别：
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

项目摘要

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/摘要侵略性行为是青年和成人精神疾病的转诊指标，由于对受害者的直接损害及其更广泛的经济影响，公共卫生的重大关注。尽管如此，由于如何，为什么和当人们积极行动时。这种异质性阻碍了建立病因的努力，确定生物底物，并产生统一的有效治疗方法。尽管激进的理论强调这是一种依赖上下文的，动态的人际行为，研究很少试图研究积极进取的它通常发生和最重要的背景（即日常生活）。当前的项目试图通过研究新的分析和在攻击行为中追求个性化医学方法所必需的测量方法研究与预防。该项目将与最先进的分析结合使用实时数据捕获解构与攻击性有关的异质行为表型的方法。为此，我们将通过来自年轻人样本的智能手机使用相关的被动感和自我报告的数据（年龄= 18-30; n = 150）被诊断出患有精神和行为障碍，侵略性风险升高。数据将在为期3周的门诊评估方案中收集。我们将应用机器学习能够发现和建模在侵略性中观察到的复杂动态过程的方法每个人（即个性化模型）的水平前瞻性预测侵略性和行为。这结果将为根据个人特定的特定时间量身定制的可扩展及时的自适应干预措施铺平道路侵略性的先例。拟议的研究将有助于NIMH战略优先级3.2 x 1）在个性化模型上，可以适应侵略性及其先例的复杂地形 2）将创新的计算方法（即机器学习）应用于多个数据流（被动感觉自我报告），以确定对侵略性个体的潜在及时干预目标。这本提案中详细介绍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，并发展成为独立的必要专业知识科学家。特别是，该候选人将接受1）个性化的心理病理学模型和挑衅的; 2）进行基于EMA的研究和建模密集纵向数据的方法； 3）通过被动传感器数据收集，处理和预测性建模。这个候选人组装了专家团队（赖特，雅各布森）和拥有专业知识来监督该项目的顾问并提供必要的培训，以支持候选人作为独立科学家的发展。 Mentalship团队的专业知识以及匹兹堡大学提供的资源，放置候选人处于实现培训，研究和职业目标的理想位置。