用知识注入注意力机制提升抑郁症评估可解释性，性能超顶尖模型7%以上。
DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment
- 融合领域知识的残差注意力架构，增强模型决策透明度。
- 在平衡与不平衡数据集上F1得分均高出现有模型7%以上。
- 适合关注心理健康监测与可解释AI的研究者与医疗科技开发者。
在当今互联社会中，社交媒体平台已成为人们表达思想、情感与情绪的重要场所，其内容为心理状态评估提供了宝贵线索。本文探讨利用Facebook、X（原Twitter）和Reddit等平台进行心理健康评估的可能性。我们提出一种名为DepressionX的领域知识注入型残差注意力模型，用于可解释的抑郁症严重程度检测。现有深度学习模型虽表现优异，但决策过程缺乏透明性，在医疗场景中尤为关键。本研究通过聚焦可解释性，弥补了这一空白，同时保持高精度。实验表明，该模型在平衡与非平衡数据集上的F1分数均超过当前最先进模型7%以上。我们的目标是建立基于社交媒体的可信、可理解精神障碍分析基础。
原文摘要 · Abstract (English)
In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, $\mathbb{X}$ (formerly Twitter), and Reddit for mental health assessments. We propose a domain knowledge-infused residual attention model called DepressionX for explainable depression severity detection. Existing deep learning models on this problem have shown considerable performance, but they often lack transparency in their decision-making processes. In healthcare, where decisions are critical, the need for explainability is crucial. In our model, we address the critical gap by focusing on the explainability of depression severity detection while aiming for a high performance accuracy. In addition to being explainable, our model consistently outperforms the state-of-the-art models by over 7% in terms of $\text{F}_1$ score on balanced as well as imbalanced datasets. Our ultimate goal is to establish a foundation for trustworthy and comprehensible analysis of mental disorders via social media.
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