Automated Risk Assessment for School Violence Prevention

项目总结 在过去的十年里，校园暴力行为有所增加，超过20%的学生报告受到欺凌 学校。校园暴力对整个学校人口，包括教职员工、学生和 家人。据指出，预防犯罪的最大成果是在给予高危青年 有效的预防计划。因此，迫切需要开发一种快速和准确的方法来 访谈学生，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攻击性，这是校园暴力的主要形式。 几种风险评估量表，从简单的临床印象到结构化的专业判断， 已提议确定青少年暴力行为。然而，这些评估在很大程度上依赖于临床医生的主题- 积极印象及其预测效度仍然是一个主要问题。此外，没有一项风险评估 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 口岸后续防范。我们的长期目标是开发一个自动风险评估(ARIA)系统 分析参与者访谈，发现高风险学生，并提供风险特征(例如， 积极、消极的想法)，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来 采访学生并评估他们的攻击性风险。这项研究的总体目标是验证我们的风险 评估方法与真实世界的证据，并开发一个AIRA系统，以自动化评估 进程。我们假设我们的风险评估方法在预测中将具有足够的预测有效性。 学校攻击性，以及利用机器学习和自然语言支持的计算机化系统- 塞辛(NLP)将能够检测高危学生，从语言方面识别与暴力相关的预测因素 帐篷，并通过协助建议改进后续预防。这一假设将由Pur-Pur验证。 提出三个具体目标：1)评估风险评估的预测效度和泛化能力 采用前瞻性收集的校本结果的方法；2)开发高性能的ARIA系统 识别校园暴力的风险特征和预测风险；以及3)比较可行的建议-- 在一项前瞻性观察性研究中，使用和不使用ARIA系统时的成绩和学校成绩。 这项研究具有很高的创新性，因为它将是利用自然语言处理和机器学习的首批努力之一 分析访谈，从学生语言中识别风险特征，并预测暴力结果。这项研究 将在几个方面产生重大影响。我们的风险评估方法在多个项目上成功验证 Sites(目标1)将提供一个有效的机制来发现青少年在学校的攻击行为。开发的AIRA系统 在AIM 2中，将能够对个别学生进行准确和可扩展的风险筛查。目标3是板凳练习赛 翻译的目标是迅速将我们的研究成果转化为临床实践。这项研究将有助于在全国范围内建立 校园暴力风险评估解决方案，这将使医疗机构、学校和学生受益。

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.