Network and machine learning analysis of childhood trauma, mental health, and AI-based emotional support needs in adolescents from underdeveloped regions.

BACKGROUND: The current intervention efficacy of generative conversational artificial intelligence (GCAI) on overall mental health issues remains limited, which may be related to the complex and unclear relation between GCAI emotional support needs (GCAI-ESN) and childhood trauma and mental health issues. METHODS: We used the Childhood Trauma Questionnaire, the Mental Health Inventory of Middle-school students, and whether there were GCAI-ESN to assess 14,380 adolescents. Machine learning (ML) combined with SHapley Additive exPlanations (SHAP) analysis was employed to identify the key predictors influencing the GCAI-ESN model. Based on the identified predictors, an undirected network was constructed and a Bayesian network analysis was conducted. RESULTS: The prevalence of childhood trauma, mental health issues, and GCAI-ESN among adolescents in underdeveloped regions was 32.43% (95% CI: 31.66%-33.19%), 16.78% (95% CI: 16.17%-17.39%), and 39.53% (95% CI: 38.73%-40.33%), respectively. The SHAP analysis of four machine learning models identified 15 key predictors for GCAI-ESN. In the undirected network model, easily anxious 'MH6' (EI = 1.07) and depressed 'MH5' (EI = 1.07) are the core nodes, while GCAI-ESN (BEI = 3.09) and family said hurtful things 'CTQ14' (BEI = 1.00) are the bridge nodes. Bayesian network analysis indicates that 'CTQ14' is the node with the most outgoing potential predictive relationships. CONCLUSIONS: This study identified the key predictors of GCAI-ESN among adolescents in underdeveloped regions and determined 'MH6', 'MH5', and 'CTQ14' as potential intervention targets when the GCAI provides emotional support, offering concrete focal points for future research.