Predictors of Long-Term School-Based Behavioral Outcomes in the Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder

Developmental Course and Risk Factors of Physical Aggression in Late Adolescence

This study examined risk factors of physical aggression during transition from early to late adolescence using a two-wave longitudinal study. Specifically, we examined if risk factors in early adolescence predict physically aggressive behavior starting in late adolescence and why some adolescents desist physical aggressive behavior while others do not. The study sample consisted of 2289 Norwegian adolescents (1235 girls) who participated in the Young-HUNT1 study (mean age 14.5) and the follow-up study 4 years later, Young-HUNT2 study (mean age 18.4). One in six young adolescents reported engaging in physical fights. Moreover, physical aggression in early adolescence was significantly associated with male gender, attention problems, academic problems, being bullied, drinking alcohol, and smoking. Male gender and heavy drinking during early adolescence increased the risk for newly emerging aggressive behavior in late adolescence, whereas heavy drinking during early adolescence was a predictor for persistent versus desisting aggressive behavior in late adolescence.

Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence

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 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 accurate and scalable computerized screening service for preventing school violence.

High Levels of Glycosaminoglycans in the Urines of Children with Attention-Deficit/Hyperactivity Disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is a common neurobehavioral/neurodevelopmental disorder. Some early studies indicated that increased intake of added sugars might have a role in ADHD. In the present study, we tested this possibility by evaluating the urinary excretion of oligosaccharides and glycosaminoglycans (GAGs) in ADHD and control subjects. Forty ADHD subjects matched with 34 controls were enrolled in the study. The subjects underwent a standardized dietary regimen. The urine levels of oligosaccharides and GAGs were quantified biochemically, and their covariance and association were evaluated statistically. Fructose (21/40, 52.5%), maltose (26/40, 65%), galactose (30/40, 75%), and lactose (38/40, 95%) excretions were frequently found in the urine of ADHD subjects (p < 0.05), an excretion which does not occur normally. Furthermore, these subjects showed a pathologic tGAG (glycosaminoglycan) excretion (40/40, 100%). The present study supports the thesis that carbohydrate metabolism differs in ADHD subjects compared with control subjects.

Automated Risk Assessment for School Violence: a Pilot Study

School violence has increased over the past ten years. This study evaluated students using a more standard and sensitive method to help identify students who are at high risk for school violence. 103 participants were recruited through Cincinnati Children's Hospital Medical Center (CCHMC) from psychiatry outpatient clinics, the inpatient units, and the emergency department. Participants (ages 12-18) were active students in 74 traditional schools (i.e. non-online education). Collateral information was gathered from guardians before participants were evaluated. School risk evaluations were performed with each participant, and audio recordings from the evaluations were later transcribed and manually annotated. The BRACHA (School Version) and the School Safety Scale (SSS), both 14-item scales, were used. A template of open-ended questions was also used. This analysis included 103 participants who were recruited from 74 different schools. Of the 103 students evaluated, 55 were found to be moderate to high risk and 48 were found to be low risk based on the paper risk assessments including the BRACHA and SSS. Both the BRACHA and the SSS were highly correlated with risk of violence to others (Pearson correlations>0.82). There were significant differences in BRACHA and SSS total scores between low risk and high risk to others groups (p-values <0.001 under unpaired t-test). In particular, there were significant differences in individual SSS items between the two groups (p-value <0.001). Of these items, Previous Violent Behavior (Pearson Correlation = 0.80), Impulsivity (0.69), School Problems (0.64), and Negative Attitudes (0.61) were positively correlated with risk to others. The novel machine learning algorithm achieved an AUC of 91.02% when using the interview content to predict risk of school violence, and the AUC increased to 91.45% when demographic and socioeconomic data were added. Our study indicates that the BRACHA and SSS are clinically useful for assessing risk for school violence. The machine learning algorithm was highly accurate in assessing school violence risk.