Automated Risk Assessment for School Violence Prevention

预防校园暴力的自动风险评估

• 批准号: 10612836
• 负责人: Drew Barzman
• 金额: $ 59.85万
• 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/10612836
• 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; Health Benefit; Healthcare; Impulsivity; Individual; Institution; Interview; Judgment; Knowledge; Language; Legal Guardians; Linguistics; Machine Learning; Manuals; Medical center; Methods; National Institute of Child Health and Human Development; Natural Language Processing; Observational Study; Outcome; Pediatric Hospitals; Performance; Population; Prevention; Prevention program; Process Assessment; Property; Psychiatrist; Public Health; Qualifying; 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; Training; Universities; Validation; Violence; Work; Youth; bullying; clinical practice; computerized; cost; deep learning model; design; emerging adult; experience; follow-up; high risk; impression; improved; individualized prevention; innovation; machine learning prediction; participant interview; predictive modeling; prevent; prospective; recruit; risk prediction; 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.

项目摘要 在过去的十年里，校园暴力行为有所增加，超过20%的学生报告说， 学校校园暴力对包括教职员工、学生和 家庭据指出，预防犯罪的最大成果发生在给予高风险青年 有效的预防方案。因此，迫切需要制定一种快速准确的方法， 访问学生，评估他们的风险特征，并为预防提供支持性证据。 我们的研究重点是检测和预防青少年攻击，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， 已被提议识别青年暴力。然而，这些评估严重依赖于临床医生的主题， 视觉印象及其预测有效性仍然是一个主要问题。此外，没有一项风险评估 包括直接分析学生使用的单词（语言），因此，提供的信息很少， 港口后续预防。我们的长期目标是开发自动风险评估（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，冲动 消极的想法），以帮助预防。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。本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，以自动评估 过程我们假设，我们的风险评估方法将有足够的预测有效性预测 攻击在学校，和一个计算机化的系统，利用机器学习和自然语言亲， cessing（NLP）将能够检测高风险的学生，从语言概念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pur- 有三个具体目标：1）评估我们的风险评估的预测有效性和普遍性 方法与前瞻性收集校本成果; 2）开发一个高性能的ARIA系统 识别风险特征并预测校园暴力的风险;以及3）比较可采取行动的措施- 在一项前瞻性观察性研究中，使用和不使用ARIA系统的情况下， 这项研究具有高度创新性，因为它将是利用NLP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，并预测暴力后果。研究 将在几个方面产生重大影响。成功验证我们的风险评估方法在多个 目的1）提供有效的机制，以侦查青少年在学校的侵犯行为。AIRA系统开发 目标2中的风险筛查将为个别学生提供准确和可扩展的风险筛查。目标3是一个板凳练习 翻译的目的是迅速将我们的发现转化为临床实践。这项研究将有助于建立一个全国性的 学校暴力风险评估的解决方案，这将有利于医疗机构，学校和学生。