Automated Risk Assessment for School Violence Prevention

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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.