ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2023
Classifying Conduct Disorder Using a Biopsychosocial Model and Machine Learning Method.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 25 citations in OpenAlex.
- Untangling relationships between cognitive development and child and adolescent mental health: Findings from the ABCD Study.Developmental cognitive neuroscience · 2026Review
- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Global Statistics of Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, and Conduct Disorder among School-Age Children from 1990-2021, with Forecasts to 2040.Neuroepidemiology · 2026Article
- Machine learning prediction of conduct problems in children using the longitudinal ABCD study.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Machine learning-based nomogram for non-suicidal self-injury among depressed adolescents: a multicentre study.Frontiers in psychiatry · 2026Article
- Entropy-based risk network identification in adolescent self-injurious behavior using machine learning and network analysis.Translational psychiatry · 2025Article
- Automated machine learning for classification and regression: A tutorial for psychologists.Behavior research methods · 2025Article
- Predicting the onset of mental health problems in adolescents.Psychological medicine · 2025Article
- Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction.BMC psychiatry · 2025Article
- Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors.Frontiers in public health · 2025Article
- Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.Frontiers in psychiatry · 2025Article
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- Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions.medRxiv : the preprint server for health sciences · 2023Article
- Antisocial behavior is associated with reduced frontoparietal network efficiency in youth.Social cognitive and affective neuroscience · 2023Article
- Different whole-brain functional connectivity correlates of reactive-proactive aggression and callous-unemotional traits in children and adolescents with disruptive behaviors.NeuroImage. Clinical · 2023Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundConduct disorder (CD) is a common syndrome with far-reaching effects. Risk factors for the development of CD span social, psychological, and biological domains. Researchers note that predictive models of CD are limited if the focus is on a single risk factor or even a single domain. Machine learning methods are optimized for the extraction of trends across multidomain data but have yet to be implemented in predicting the development of CD.
methodsSocial (e.g., family, income), psychological (e.g., psychiatric, neuropsychological), and biological (e.g., resting-state graph metrics) risk factors were measured using data from the baseline visit of the Adolescent Brain Cognitive Development Study when youth were 9 to 10 years old (N = 2368). Applying a feed-forward neural network machine learning method, risk factors were used to predict CD diagnoses 2 years later.
resultsA model with factors that included social, psychological, and biological domains outperformed models representing factors within any single domain, predicting the presence of a CD diagnosis with 91.18% accuracy. Within each domain, certain factors stood out in terms of their relationship to CD (social: lower parental monitoring, more aggression in the household, lower income; psychological: greater attention-deficit/hyperactivity disorder and oppositional defiant disorder symptoms, worse crystallized cognition and card sorting performance; biological: disruptions in the topology of subcortical and frontoparietal networks).
conclusionsThe development of an accurate, sensitive, and specific predictive model of CD has the potential to aid in prevention and intervention efforts. Key risk factors for CD appear best characterized as reflecting unpredictable, impulsive, deprived, and emotional external and internal contexts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.