ArticleTranslational psychiatry2025
Entropy-based risk network identification in adolescent self-injurious behavior using machine learning and network analysis.
Article in Translational psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The relationship between role clarity and compulsory citizenship behavior among junior nurses: evidence from network analysis and simulation-based network modeling.Frontiers in public health · 2026Article
- Machine learning-based nomogram for non-suicidal self-injury among depressed adolescents: a multicentre study.Frontiers in psychiatry · 2026Article
- Smartwatch and physical education exam stress: the role of exercise self-efficacy and physical self-concept.Frontiers in psychiatry · 2026Article
- Predicting Suicide Attempt Trends in Youth: A Machine Learning Analysis Using Google Trends and Historical Data.Journal of clinical medicine · 2025Article
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Authors and funding
6 authors.
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Abstract
Adolescent Self-Injurious Behavior (SIB) is a significant global public health issue, with a lifetime prevalence rate of approximately 13.7%. As awareness of SIB rises, there is an urgent need for effective prediction mechanisms to enable early identification and intervention, reducing the risk of suicide and self-harm attempts. This study, grounded in Psychopathological Network Theory, uses machine learning and network analysis to explore the multidimensional structure of risk factors for adolescent SIB. A survey of 2047 adolescents aged 11 to 17 years in China analyzed 19 variables across physiological, psychological, and social domains. The Entropy Weight Method (EWM) was applied to combine network analysis and machine learning outcomes for a comprehensive risk evaluation. The study identified key risk factors for SIB, including loneliness, ADHD symptoms, Internet addiction, anxiety, depression, affinity for solitude, autistic traits, being bullied. These factors interact within a complex network structure, influencing the occurrence of SIB both directly and indirectly. The integration of EWM, network analysis, and machine learning provides a more precise risk assessment approach for adolescent SIB. The findings offer valuable insights into the causal mechanisms of SIB and emphasize the importance of targeted prevention and intervention strategies.
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