Automated Risk Assessment for School Violence Prevention

预防校园暴力的自动风险评估

• 批准号: 10096109
• 负责人: Drew Barzman
• 金额: $ 45.8万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10096109
• 关键词: Achievement; Adolescent; Aggressive behavior; Area; Attitude; Behavior; Characteristics; Childhood; Clinical; Crime; Data; Data Set; Descriptor; Development; Dropout; Early Intervention; Etiology; Evaluation; Family; Feeling; Foundations; Geographic Locations; Goals; Healthcare; Impulsivity; Individual; Institution; Interview; Judgment; Knowledge; Language; Legal Guardians; Linguistics; Machine Learning; Manuals; Medical center; Methods; Modeling; National Institute of Child Health and Human Development; Natural Language Processing; Observational Study; Outcome; Participant; Pediatric Hospitals; Performance; Population; Prevention; Prevention program; Process Assessment; Property; Psychiatrist; Public Health; Questionnaires; ROC Curve; Randomized; Recommendation; Reporting; Research; Research Priority; Risk; Risk Assessment; Risk Factors; Safety; Sampling; Schools; Scientist; Site; Structure; Students; System; Technology; Testing; Thinking; Time; Training; Universities; Validation; Violence; Work; Youth; base; bullying; clinical practice; computerized; cost; deep learning; design; emerging adult; experience; follow-up; high risk; impression; improved; individualized prevention; innovation; predictive modeling; prevent; prospective; recruit; school violence; school violence prevention; screening; study population; youth violence

PROJECT SUMMARY Acts of school violence have increased over the past decade and over 20% of students report being bullied at school. School violence has a far-reaching impact on the entire school population, including staff, students and families. It was noted that the largest crime-prevention results occurred when youth at elevated risk were given effective prevention programs. As such, there is a critical need for developing a rapid and accurate approach to interview students, assess their risk characteristics, and provide supportive evidence for prevention. Our study focuses on detecting and preventing youth aggression, the predominant form of school violence. Several risk assessment scales, ranging from simple clinical impressions to structured professional judgments, have been proposed to identify youth violence. However, these assessments heavily rely on clinicians' subjec- tive impressions and their predictive validities remain a major issue. In addition, none of the risk assessments include direct analysis of the words (language) used by students and hence, provide little information to sup- port subsequent prevention. Our long-term goal is to develop an Automated RIsk Assessment (ARIA) system to analyze participant interviews, detect elevated-risk students, and provide risk characteristics (e.g., impul- sivity, negative thoughts) to assist prevention. In our earlier study we developed a risk assessment approach to interview students and evaluate their risk of aggression. The overall objective of this study is to validate our risk assessment approach with real-world evidence, and to develop an AIRA system to automate the assessment process. We hypothesize that our risk assessment approach will have sufficient predictive validity in predicting aggression at school, and a computerized system leveraging machine learning and natural language pro- cessing (NLP) will be able to detect high-risk students, identify violence-related predictors from linguistic con- tent, and improve subsequent prevention by assisting recommendations. The hypothesis will be tested by pur- suing three specific aims: 1) Evaluate the predictive validity and generalizability of our risk assessment approach with prospectively collected school-based outcomes; 2) Develop a high-performing ARIA system to identify risk characteristics and predict risk of school violence; and 3) Compare actionable recommenda- tions and school outcomes with and without using the ARIA system in a prospective observational study. The study is highly innovative in that it will be among the first efforts that leverage NLP and machine learning to analyze interviews, identify risk characteristics from student language, and predict violence outcomes. The study will have a significant impact on several fronts. Successful validation of our risk assessment approach on multiple sites (Aim 1) will provide a valid mechanism to detect youth aggression at school. The AIRA system developed in Aim 2 will enable accurate and scalable risk screening for individual students. Aim 3 is a bench-to-practice translational aim to rapidly transfer our findings to clinical practice. The study will help establish a nationwide solution for school violence risk assessment, which will benefit healthcare institutions, schools, and students.

项目概要 过去十年，校园暴力行为有所增加，超过 20% 的学生表示在学校遭受欺凌。 学校。校园暴力对整个学校人口产生深远的影响，包括教职员工、学生和 家庭。有人指出，当向高危青少年提供帮助时，预防犯罪的效果最大。 有效的预防计划。因此，迫切需要开发一种快速、准确的方法来 采访学生，评估他们的风险特征，并为预防提供支持性证据。 我们的研究重点是发现和预防青少年的攻击行为，这是校园暴力的主要形式。 多种风险评估量表，从简单的临床印象到结构化的专业判断， 已提议识别青少年暴力。然而，这些评估在很大程度上依赖于临床医生的主题 印象及其预测有效性仍然是一个主要问题。此外，没有任何风险评估 包括对学生使用的单词（语言）的直接分析，因此提供的信息很少 端口后续预防。我们的长期目标是开发自动化风险评估（ARIA）系统 分析参与者访谈，检测高风险学生，并提供风险特征（例如，冲动） 积极性、消极想法）以协助预防。在我们早期的研究中，我们开发了一种风险评估方法 采访学生并评估他们的攻击风险。这项研究的总体目标是验证我们的风险 评估方法与现实世界的证据，并开发一个 AIRA 系统来自动化评估 过程。我们假设我们的风险评估方法在预测方面将具有足够的预测有效性 学校里的攻击性行为，以及利用机器学习和自然语言亲和力的计算机化系统 cessing（NLP）将能够检测高风险学生，从语言环境中识别与暴力相关的预测因素 帐篷，并通过协助建议改善后续预防。该假设将通过目的进行检验 提出三个具体目标：1）评估我们的风险评估的预测有效性和普遍性 采用前瞻性收集的校本成果； 2）开发高性能ARIA系统 识别风险特征并预测校园暴力风险； 3）比较可行的建议 在前瞻性观察研究中使用和不使用 ARIA 系统的情况和学校成绩。 该研究具有高度创新性，因为它将是利用 NLP 和机器学习来 分析访谈，从学生语言中识别风险特征，并预测暴力结果。研究 将在多个方面产生重大影响。我们的风险评估方法在多个方面的成功验证 网站（目标 1）将提供有效的机制来检测青少年在学校的攻击行为。 AIRA系统开发 目标 2 将为个别学生提供准确且可扩展的风险筛查。目标 3 是一个基础练习 转化目标是将我们的发现快速转化为临床实践。这项研究将有助于建立一个全国性的 校园暴力风险评估解决方案，将惠及医疗机构、学校和学生。