An interpretable machine learning model for predicting sleep disturbances in adolescents with mood disorders: the key role of psychological factors

作者：Xiaohong Chu, Xudong Yang, Zixin Ye, Xinyu Hu, Lili Chen, Lili Chen, Yawen Zheng, Yawen Zheng, Li Chen, Li Chen · 发表于：BMC Psychiatry · 年份：2025 · DOI：10.1186/s12888-025-07659-6 · 被引用次数：2 · 研究领域：Sleep and related disorders、Sleep and Wakefulness Research、Sleep and Work-Related Fatigue

OBJECTIVE: Sleep 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. METHODS: This 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. RESULTS: The 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. CONCLUSION: An explainable machine learning model exhibited high predictive performance in identifying the risk...