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Juvenile Delinquency

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Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders.

NeuroImage
Classification models are becoming useful tools for finding patterns in neuroimaging data sets that are not observable to the naked eye. Many of these models are applied to discriminating clinical groups such as schizophrenic patients from healthy co...

Can Machine Learning Improve Screening for Targeted Delinquency Prevention Programs?

Prevention science : the official journal of the Society for Prevention Research
The cost-effectiveness of targeted delinquency prevention programs for children depends on the accuracy of the screening process. Screening accuracy is often poor, resulting in wasted resources and missed opportunities to avert negative outcomes. Thi...

Identifying multilevel predictors of trajectories of psychopathology and resilience among juvenile offenders: A machine learning approach.

Development and psychopathology
Mental ill health is more common among juvenile offenders relative to adolescents in general. Little is known about individual differences in their long-term psychological adaptation and its predictors from multiple aspects of their life. This study ...