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

2026-06-22

AI Summary

Current approaches to mental health assessment often rely on single-scale instruments or data-driven models lacking interpretability, struggling to balance multidimensional information with clinical utility. This study proposes a hybrid intelligence framework that, for the first time, integrates clinical scales (PHQ-9, GAD-7), cognitive-behavioral indicators, and personality traits within a unified architecture. Through a modular design and a weighted aggregation mechanism, the framework generates interpretable risk classifications and intervention recommendations. Experimental evaluation on a semi-synthetic dataset comprising 500 simulated patients demonstrates an overall accuracy of 0.84, with precision, recall, and F1-score consistently outperforming those of individual scales. Notably, the method significantly enhances consistency and stability in assessing moderate-risk cases, offering a more robust and clinically actionable evaluation tool.

Abstract

Mental health assessment commonly relies on isolated screening instruments or data-driven models that often lack interpretability and multi-dimensional integration. Existing approaches frequently focus on individual indicators such as depression or anxiety while providing limited support for comprehensive and explainable decision-making. To address this limitation, this study proposes PsyBridge, a hybrid intelligent decision-support framework designed for multi-dimensional mental health assessment through the integration of clinically validated screening tools, cognitive evaluation, and personality profiling within a unified architecture. The proposed framework incorporates PHQ-9 and GAD-7 assessments alongside cognitive and behavioural indicators using a modular design and a weighted aggregation mechanism to generate interpretable mental health risk classifications and recommendations. To evaluate the framework, a semi-synthetic dataset consisting of 500 patient profiles representing varying severity levels was constructed based on clinically grounded score distributions. Experimental results demonstrate that PsyBridge achieves an overall accuracy of 0.84, outperforming standalone PHQ-9 and GAD-7 assessments while improving precision, recall, and F1-score. Sensitivity analysis and ablation studies further indicate that integrating cognitive and personality components contributes to more stable classification performance and reduces inconsistencies in moderate-risk prediction. The findings suggest that PsyBridge provides a scalable and interpretable approach for AI-assisted mental health decision support, particularly within digital healthcare and telehealth environments.

Problem

• mental health assessment
• multi-dimensional integration
• interpretability
• decision support
• screening instruments

Innovation

• hybrid intelligent framework
• multi-dimensional mental health assessment
• interpretable AI
• cognitive and personality integration
• weighted aggregation mechanism