心理健康评估通常依赖于孤立的筛查工具或数据驱动的模型，而这些工具往往缺乏可解释性和多维整合。现有的方法经常关注抑郁或焦虑等个体指标，而为全面且可解释的决策提供有限的支持。为了解决这一局限性,，本研究提出了 PsyBridge, 一种混合智能决策支持框架，旨在通过将经过临床验证的筛查工具, 认知评估, 和个性分析整合在统一的架构中，进行多维心理健康评估。拟议的框架将 PHQ-9 和 GAD-7 评估与认知和行为指标结合起来，使用模块化设计和加权聚合机制来生成可解释的心理健康风险分类和建议。为了评估框架,，根据临床评分分布构建了一个半合成数据集，该数据集由代表不同严重程度的 500 名患者资料组成。实验结果表明，PsyBridge 的总体准确度为 0.84,，优于独立的 PHQ-9 和 GAD-7 评估，同时提高了精确度, 召回率, 和 F1 分数。敏感性分析和消融研究进一步表明，整合认知和个性成分有助于更稳定的分类性能，并减少中等风险预测的不一致。研究结果表明，PsyBridge 为人工智能辅助心理健康决策支持,提供了一种可扩展且可解释的方法，特别是在数字医疗和远程医疗环境中。
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.
科目: 人工智能 (cs.AI); 机器学习 (cs.LG)
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)