This study introduces PsyBridge, a hybrid intelligent framework designed to address the limitations of isolated mental health screening tools by integrating clinically validated assessments with cognitive and personality profiling. The system utilizes a modular architecture and weighted aggregation mechanism to generate interpretable risk classifications and decision support recommendations.
- Combines PHQ-9 and GAD-7 assessments with cognitive and behavioral indicators within a unified architecture.
- Evaluated on a semi-synthetic dataset of 500 patient profiles representing varying severity levels.
- Achieves an overall accuracy of 0.84, outperforming standalone PHQ-9 and GAD-7 assessments.
- Improves precision, recall, and F1-score while reducing inconsistencies in moderate-risk prediction through sensitivity analysis.
The findings suggest that PsyBridge offers a scalable and interpretable approach for AI-assisted mental health decision support, particularly within digital healthcare and telehealth environments.