ArticleBMC psychiatry2025
An interpretable machine learning model for predicting sleep disturbances in adolescents with mood disorders: the key role of psychological factors.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Association Between Mental Health Literacy and Its Dimensions with Adolescent Depression and Anxiety: A Cross-Sectional Study Among 5759 Adolescents in China.Behavioral sciences (Basel, Switzerland) · 2026Article
- The mediation effects of smartphone addiction, negative emotion and psychological resilience in the relationship between social support and sleep quality among medical students.Frontiers in psychiatry · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
objectiveSleep disturbances are severe in adolescents with mood disorders, yet accurate and interpretable prediction tools for this high-risk population are lacking. This study aimed to develop and validate an explainable machine learning model to predict sleep disturbances risk and identify key predictors in a large clinical cohort.
methodsThis cross-sectional study included 1,505 adolescents with mood disorders, recruited from multiple hospital centers across China. The Pittsburgh Sleep Quality Index Scale served as the primary outcome measure for assessing sleep disturbances. A wide range of potential predictors was evaluated, spanning five domains: socio-demographic, psychological, behavioral, familial, and educational factors. We used the Boruta algorithm for feature selection, compared six machine learning models, and employed SHAP for model interpretation.
resultsThe Random Forest model demonstrated superior performance, achieving an AUC of 0.941 (95% CI: 0.915–0.966), with an accuracy of 89.56%, a sensitivity of 0.980, and a specificity of 0.350. SHAP analysis identified psychological suffering and perceived discrimination in school as the most significant risk factors, while psychological resilience and core self-evaluations emerged as key protective factors. Additionally, the analysis indicated a non-linear impact of these factors on individual risk.
conclusionAn explainable machine learning model exhibited high predictive performance in identifying the risk of sleep disturbances among adolescents with mood disorders. Our findings underscore the importance of both internal psychological states and adverse school experiences as critical intervention targets. This approach establishes a scientific foundation for clinical tools that facilitate the transition from generic advice to personalized, mechanism-targeted care, thereby advancing precision mental healthcare for youth.
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