AI-DRIVEN PRECISION MENTAL HEALTH FRAMEWORK USING MULTIMODAL BEHAVIORAL ASSESSMENT, EXPLAINABLE MACHINE LEARNING, AND PERSONALIZED PSYCHOLOGICAL INTERVENTION PREDICTION FOR EARLY DETECTION AND PERSONALIZED TREATMENT OF MENTAL DISORDERS

Abstract

Mental disorders frequently remain undetected until symptoms become clinically significant, while conventional diagnostic approaches rely on intermittent assessments, subjective reporting, and trial-and-error treatment selection. This study proposes an AI-driven precision mental health framework integrating multimodal behavioral assessment, explainable machine learning, and personalized psychological intervention prediction to improve early identification, diagnostic differentiation, and individualized treatment matching. The framework combines four complementary data modalities: smartphone and wearable sensing, speech acoustics, linguistic behavior, and standardized self-report assessments. A reproducible simulated cohort comprising 2,400 participants from three recruitment sites was developed, including 952 healthy individuals, 645 participants with depression, 494 with anxiety, and 309 with bipolar disorder. Twenty-one behavioral and psychological features were integrated using a gradient-boosted learning architecture, while permutation-based feature attribution enhanced interpretability. Model performance was evaluated through stratified testing, five-fold cross-validation, cross-site generalization, longitudinal monitoring, subgroup analysis, and simulated randomized treatment allocation.

Experimental findings demonstrated strong discrimination across psychiatric categories, with the multimodal model achieving a macro-averaged area under the curve (AUC) of 0.960 (95% CI: 0.949–0.970), macro F1-score of 0.832, and classification accuracy of 85.0% on 600 held-out participants. Individual behavioral modalities achieved AUC values of 0.762–0.777, compared with 0.899 for self-report assessments. Cross-validation produced an AUC of 0.962 ± 0.003, while cross-site performance ranged from 0.954 to 0.973. Explainability analysis identified GAD-7 scores and mood variability as influential predictors of anxiety and bipolar disorder, respectively. Longitudinal monitoring detected 99.7% of symptomatic participants at 95% specificity, providing an average warning lead of 2.64 weeks under simulation-defined onset assumptions. Eight-week multimodal monitoring increased sensitivity to 88.1%. Personalized treatment matching improved simulated response rates from 39.1% to 53.2%, representing a 14.1-percentage-point improvement. These simulation-based findings demonstrate methodological feasibility rather than established clinical effectiveness and support prospective validation using real-world psychiatric cohorts.