Finding Warning Markers: Leveraging Natural Language Processing and Machine Learning Technologies to Detect Risk of School Violence
In: Int J Med Inform, 2020
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Zugriff:
INTRODUCTION: 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 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. MATERIAL AND METHODS: We prospectively recruited 131 students with or without behavioral concerns from 89 schools between 05/01/2015 and 04/30/2018. The subjects were interviewed with two risk assessment scales and a questionnaire, and their risk of violence were determined by pediatric psychiatrists based on clinical judgment. Using 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’ sociodemographic 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 .
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Finding Warning Markers: Leveraging Natural Language Processing and Machine Learning Technologies to Detect Risk of School Violence
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Autor/in / Beteiligte Person: | Ni, Yizhao ; Barzman, Drew ; Bachtel, Alycia ; Griffey, Marcus ; Osborn, Alexander ; Sorter, Michael |
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Zeitschrift: | Int J Med Inform, 2020 |
Veröffentlichung: | 2020 |
Medientyp: | academicJournal |
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