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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Sunil Wanjari, Manish Thakre, Aayushi Asole, Sharwari Raut, Kwabena Adu-Duodu, Yinhao Li, Stanly Wilson
• Venue: arXiv: cs.AI
• Published: 2026-06-22
• Collected: 2026-06-23
• URL: http://arxiv.org/abs/2606.23673v1
• PDF: https://arxiv.org/pdf/2606.23673v1
• DOI: UNKNOWN

Plain-Language Brief

The study proposes a new framework called PsyBridge, designed to improve mental health assessment by integrating multiple dimensions, including clinically validated screening tools, cognitive evaluation, and personality profiling. The framework uses PHQ-9 and GAD-7 assessments along with cognitive and behavioral indicators to generate interpretable mental health risk classifications and recommendations. Experimental results show that PsyBridge outperforms standalone assessments in terms of accuracy, precision, recall, and F1-score.

What the Document Says

• The study presents a framework named PsyBridge for multi-dimensional mental health assessment.
• PsyBridge integrates clinically validated screening tools (PHQ-9 and GAD-7), cognitive evaluation, and personality profiling.
• It uses a modular design and weighted aggregation mechanism.
• The framework generates interpretable mental health risk classifications and recommendations.
• A semi-synthetic dataset of 500 patient profiles was created for evaluation.
• Experimental results show that PsyBridge achieves an overall accuracy of 0.84 and outperforms standalone PHQ-9 and GAD-7 assessments.
• The framework improves precision, recall, and F1-score compared to standalone assessments.

Key Concepts Mentioned

• mental health assessment
• PsyBridge
• PHQ-9
• GAD-7
• cognitive evaluation
• personality profiling
• semi-synthetic dataset

Claims Made by the Document

• PsyBridge improves mental health assessment.
• PsyBridge outperforms standalone PHQ-9 and GAD-7 assessments.

Evidence Used

• experimental results demonstrating accuracy, precision, recall, and F1-score

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.