Machine learning-based early warning model for adolescent mental health risk using the p factor / Yunjing LI in Journal of Child Psychology and Psychiatry, 67-11 (November 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-11 (November 2026) . - p.1906-1917 Titre : Machine learning-based early warning model for adolescent mental health risk using the p factor Type de document : texte imprimé Auteurs : Yunjing LI, Auteur ; Wenxuan BIAN, Auteur ; Xiaohong WEN, Auteur ; Yuanyuan FANG, Auteur ; Yihan WANG, Auteur ; Haijiang LI, Auteur Article en page(s) : p.1906-1917 Langues : Anglais (eng) Mots-clés : Adolescents  p factor  machine learning  mental health Index. décimale : PER Périodiques Résumé : Background Accurate identification of adolescents at high mental health risk is essential for timely intervention and improved service delivery. The general factor of psychopathology (p factor), capturing shared variance across mental disorders, was used as a transdiagnostic outcome to develop a machine learning model for risk identification. Methods Data from 5,283 Chinese adolescents were used for model training and testing, with an additional 968 participants for external validation. Guided by ecological systems theory, a multidimensional early warning framework covering 59 indicators across individual, school, family, and society domains was constructed. Seven machine learning algorithms were tested using the p factor as the outcome. Shapley Additive exPlanations (SHAP) values ranked predictor importance and identified an optimal feature subset. Results The XGBoost model showed the best performance (macro F1?=?0.73 on internal validation, 0.80 on external validation). The final model used 23 predictors. SHAP confirmed sleep quality as the most influential factor, followed by repetitive negative thinking, interpersonal stress, impulsivity, and emotional intensity. Conclusions This study suggests that a p-factor-based machine learning model can effectively identify adolescents at mental health risk, with sleep quality emerging as the most influential predictor, suggesting potential intervention targets for early prevention. En ligne : https://doi.org/10.1111/jcpp.70194 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=594 [article] Machine learning-based early warning model for adolescent mental health risk using the p factor [texte imprimé] / Yunjing LI, Auteur ; Wenxuan BIAN, Auteur ; Xiaohong WEN, Auteur ; Yuanyuan FANG, Auteur ; Yihan WANG, Auteur ; Haijiang LI, Auteur . - p.1906-1917. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-11 (November 2026) . - p.1906-1917 Mots-clés : Adolescents  p factor  machine learning  mental health Index. décimale : PER Périodiques Résumé : Background Accurate identification of adolescents at high mental health risk is essential for timely intervention and improved service delivery. The general factor of psychopathology (p factor), capturing shared variance across mental disorders, was used as a transdiagnostic outcome to develop a machine learning model for risk identification. Methods Data from 5,283 Chinese adolescents were used for model training and testing, with an additional 968 participants for external validation. Guided by ecological systems theory, a multidimensional early warning framework covering 59 indicators across individual, school, family, and society domains was constructed. Seven machine learning algorithms were tested using the p factor as the outcome. Shapley Additive exPlanations (SHAP) values ranked predictor importance and identified an optimal feature subset. Results The XGBoost model showed the best performance (macro F1?=?0.73 on internal validation, 0.80 on external validation). The final model used 23 predictors. SHAP confirmed sleep quality as the most influential factor, followed by repetitive negative thinking, interpersonal stress, impulsivity, and emotional intensity. Conclusions This study suggests that a p-factor-based machine learning model can effectively identify adolescents at mental health risk, with sleep quality emerging as the most influential predictor, suggesting potential intervention targets for early prevention. En ligne : https://doi.org/10.1111/jcpp.70194 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=594

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