Beyond Semantics: An Evidential Reasoning-Aware Multi-View Learning Framework for Trustworthy Mental Health Prediction

Problem
This preprint addresses the limitations of existing automated mental health prediction models that primarily rely on semantic representations, which often yield overconfident predictions in the presence of ambiguous, noisy, or shifted data. The lack of reliable uncertainty estimation in these models poses significant challenges for deployment in high-stakes environments, where trustworthiness is paramount. The authors aim to bridge this gap by proposing a framework that integrates multiple views of data to enhance both predictive performance and uncertainty quantification.

Method
The authors introduce a multi-view learning framework that combines semantic information from encoder-only models with higher-level reasoning from decoder-only models. This approach leverages an evidential learning framework based on Subjective Logic to model uncertainty explicitly. The framework employs an evidential fusion strategy that balances the contributions of complementary views while discounting unreliable evidence. The architecture integrates both encoder and decoder components, although specific details regarding the model architecture, loss functions, and training compute are not disclosed. The training is conducted on three real-world datasets: Dreaddit, SDCNL, and DepSeverity, focusing on the generation of reasoning-aware representations and uncertainty estimates.

Results
The proposed framework achieves accuracies of 0.835 on the Dreaddit dataset, 0.731 on SDCNL, and 0.751 on DepSeverity. These results indicate a significant improvement over baseline models, although specific baseline performance metrics are not provided in the summary. The authors also report enhanced robustness to noise and improved interpretability through case studies, suggesting that the framework not only boosts predictive accuracy but also offers trustworthy uncertainty estimates and human-understandable reasoning signals.

Limitations
The authors acknowledge that their approach may still be sensitive to the quality of input data and the inherent biases present in the datasets used. They do not discuss potential scalability issues or the computational overhead introduced by the evidential learning framework. Additionally, the reliance on multi-view learning may complicate the model’s deployment in real-world scenarios where data availability and quality can vary significantly.

Why it matters
This work has significant implications for the field of mental health assessment, particularly in developing AI systems that can operate reliably in risk-sensitive contexts. By providing a framework that not only enhances predictive performance but also quantifies uncertainty, the authors contribute to the growing need for trustworthy AI applications in healthcare. The integration of reasoning-aware representations could facilitate more interpretable models, fostering greater acceptance and trust among practitioners and patients alike. Future research could build on this framework to explore its applicability across other domains requiring robust uncertainty quantification.

Authors: Yucheng Ruan, Ling Huang, Qika Lin, Kai He, Mengling Feng
Source: arXiv:2605.05121
URL: https://arxiv.org/abs/2605.05121v1