JMIR Publications

ABSTRACT

Background:

School violence has a far reaching effect, impacting the entire school population including staff, students and their families. Among youth attending the most violent schools, studies have reported higher dropout rates, poor school attendance, and poor scholastic achievement. It was noted that the largest crime-prevention results occurred when youth at elevated risk were given an individualized prevention program. However, much work is needed to establish an effective approach to identify at-risk subjects.

Objective:

In our earlier research, we developed a standardized risk assessment program to interview subjects, identify risk and protective factors, and evaluate risk for school violence. This study focused on developing natural language processing (NLP) and machine learning technologies to automate the risk assessment process.

Methods:

We prospectively recruited 131 students with behavioral concerns from 89 schools between 05/01/2015 and 04/30/2018. The subjects were interviewed with three innovative risk assessment scales and their risk of violence were determined by pediatric psychiatrists based on clinical judgment. Leveraging NLP technologies, different types of linguistic features were extracted from the interview content. Machine learning classifiers were then applied to predict risk of school violence for individual subjects. A two-stage feature selection was implemented to identify violence-related predictors. The performance was validated on the psychiatrist-generated reference standard of risk levels, where positive predictive value (PPV), sensitivity (SEN), negative predictive value (NPV), specificity (SPEC) and area under the ROC curve (AUC) were assessed.

Results:

Compared to subjects' demographics and socioeconomic information, use of linguistic features significantly improved classifiers' predictive performance (P<0.01). The best-performing classifier with n-gram features achieved 86.5%/86.5%/85.7%/85.7%/94.0% (PPV/SEN/NPV/SPEC/AUC) on the cross-validation set and 83.3%/93.8%/91.7%/78.6%/94.6% (PPV/SEN/NPV/SPEC/AUC) on the test data. The feature selection process identified a set of predictors covering the discussion of subjects' thoughts, perspectives, behaviors, individual characteristics, peers and family dynamics, and protective factors.

Conclusions:

By analyzing the content from subject interviews, the NLP and machine learning algorithms showed good capacity for detecting risk of school violence. The feature selection uncovered multiple warning markers that could deliver useful clinical insights to assist personalizing intervention. Consequently, the developed approach offered the promise of an end-to-end computerized screening service for preventing school violence.