PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support

PsyBridge is a hybrid AI framework that integrates validated mental health screening tools (PHQ-9, GAD-7) with cognitive and personality assessments to provide interpretable, multi-dimensional mental health risk classification. The framework achieved 84% accuracy on a 500-patient semi-synthetic dataset, outperforming isolated screening instruments and demonstrating potential for digital healthcare and telehealth applications.

PsyBridge addresses a critical gap in mental health assessment by moving beyond fragmented, single-indicator approaches toward comprehensive, explainable AI-assisted diagnostics. The framework's innovation lies in its modular architecture that combines clinically validated instruments with behavioral and cognitive profiling through weighted aggregation, enabling practitioners to understand both what risk level a patient presents and why. This interpretability differentiates it from black-box machine learning models that healthcare providers often distrust in clinical settings.

The research reflects growing recognition that mental health exists across multiple dimensions—depression, anxiety, cognitive function, and personality traits are interconnected factors requiring simultaneous evaluation. Traditional screening tools like PHQ-9 and GAD-7, while valuable, operate in isolation and miss crucial contextual information. The semi-synthetic dataset methodology, while limiting real-world validation claims, allows for controlled evaluation across severity levels and establishes baseline performance metrics.

For digital health stakeholders—EHR vendors, telehealth platforms, and mental health startups—PsyBridge represents a marketable differentiator in increasingly competitive spaces. The framework's scalability and interpretability align with regulatory trends favoring explainable AI in healthcare, where FDA scrutiny and clinical adoption barriers make transparency essential. Sensitivity analyses showing improved stability in moderate-risk prediction suggest practical utility in cases where misclassification carries highest consequences.

Future validation requires prospective clinical trials with real patient data and practitioner feedback loops to confirm whether theoretical improvements translate to clinical outcomes and reduce assessment burden in actual practice settings.

• →PsyBridge achieves 84% accuracy by integrating PHQ-9, GAD-7, and cognitive-behavioral indicators rather than relying on isolated screening tools.
• →The framework's modular, weighted-aggregation design produces interpretable risk classifications that healthcare practitioners can understand and trust.
• →Ablation studies confirm that cognitive and personality components reduce prediction inconsistencies, particularly for moderate-risk cases.
• →The approach is designed for scalability within digital healthcare and telehealth environments where explainability and efficiency are critical.
• →Real-world clinical validation remains needed to confirm whether performance gains in semi-synthetic testing translate to improved patient outcomes.