Automated mental health prediction using textual data has shown promising results with deep learning and large language models. However, deploying these models in high-stakes real-world settings remains challenging, as existing approaches largely rely on semantic representations and often produce overconfident predictions under ambiguous, noisy, or shifted data. Moreover, most methods lack reliable uncertainty estimation, undermining trust in risk-sensitive mental health applications. To address these limitations, we formulate the task as a multi-view learning problem that integrates semantic information from encoder-only models with higher-level reasoning information from decoder-only models, where reasoning-aware representations and uncertainty modeling are obtained in a trustworthy manner. To ensure reliable fusion, we adopt an evidential learning framework based on Subjective Logic to explicitly model uncertainty and introduce an evidential fusion strategy that balances complementary views while discounting unreliable evidence. Benchmarking on three real-world datasets, Dreaddit, SDCNL, and DepSeverity, reports accuracies of 0.835, 0.731, and 0.751, respectively, demonstrating its potential for reliable mental health prediction. Additional experiments on robustness to noise and case studies for interpretability confirm that our proposed framework not only improves predictive performance but also provides trustworthy uncertainty estimates and human-understandable reasoning signals, making it suitable for risk-sensitive applications in mental health assessment.

翻译：基于文本数据的自动化心理健康预测在深度学习和大型语言模型的应用下已展现出显著进展。然而，在现实高风险场景中部署这些模型仍面临挑战：现有方法主要依赖语义表征，在模糊、含噪或分布偏移的数据条件下常产生过度自信的预测。此外，多数方法缺乏可靠的uncertainty估计，削弱了其在敏感性心理健康应用中的可信度。为克服这些局限，我们将任务形式化为多视角学习问题，整合基于编码器模型的语义信息与基于解码器模型的高阶推理信息，以可信方式同时获取推理论证感知表征与不确定建模。为确保融合可靠性，我们采用基于主观逻辑的证据学习框架显式建模不确定性，并提出一种证据融合策略，该策略在平衡互补视角的同时，对不可靠证据进行折扣处理。在Dreaddit、SDCNL和DepSeverity三个真实数据集上的基准测试分别达到0.835、0.731和0.751的准确率，证明了该方法在可靠心理健康预测中的潜力。噪声鲁棒性实验与可解释性案例研究进一步证实：我们提出的框架不仅提升预测性能，还能提供可信的不确定性估计与人类可理解的推理信号，使其适用于心理健康评估中的风险敏感型应用。