PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support
2026-06-22 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors created PsyBridge, a new system that combines different mental health tests and personal traits to better assess people's mental health. Unlike using just one test, PsyBridge uses multiple tools together to give clearer and more reliable results about mental health risks. They tested it on a made-up group of 500 people and found it worked better than using common tests alone. The study shows that adding thinking and personality information helps make clearer decisions, especially for people with moderate symptoms. PsyBridge could be useful in digital health and online care settings.
PHQ-9GAD-7mental health assessmentcognitive evaluationpersonality profilingmachine learningdecision support systemsprecision and recallsemi-synthetic datatelehealth
Authors
Sunil Wanjari, Manish Thakre, Aayushi Asole, Sharwari Raut, Kwabena Adu-Duodu, Yinhao Li, Stanly Wilson
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.