用对话检测焦虑抑郁，支持多标签且可解释。
Detecting anxiety and depression in dialogues: a multi-label and explainable approach
- 结合大语言模型与机器学习进行多标签分类
- 在真实数据集上达到90%准确率，优于已有方法
- 提供可视化决策解释，适合医疗辅助系统使用
焦虑和抑郁是全球最常见的心理健康问题，影响着相当比例的人群。为此，政府卫生系统等利益相关方正从整体视角推动早期检测与预防策略（即同时应对多种障碍）。本文提出一种全新的多标签分类系统，用于识别用户与聊天机器人互动中的焦虑与抑郁状态。输入为对话数据，创新性地利用大语言模型（LLMs）进行特征提取，以应对语言的复杂性与多样性。结合LLMs的语言理解能力与机器学习模型基于标注数据的上下文知识，形成高效的心理健康评估方法。为提升系统的可信度、可靠性和可问责性，设计了图形化仪表盘，提供模型决策的可解释描述。在真实数据集上的实验结果表明，准确率达90%，优于现有文献水平。最终目标是实现早于正式医疗干预前的可访问、可扩展的辅助诊断。
原文摘要 · Abstract (English)
Anxiety and depression are the most common mental health issues worldwide, affecting a non-negligible part of the population. Accordingly, stakeholders, including governments' health systems, are developing new strategies to promote early detection and prevention from a holistic perspective (i.e., addressing several disorders simultaneously). In this work, an entirely novel system for the multi-label classification of anxiety and depression is proposed. The input data consists of dialogues from user interactions with an assistant chatbot. Another relevant contribution lies in using Large Language Models (LLMs) for feature extraction, provided the complexity and variability of language. The combination of LLMs, given their high capability for language understanding, and Machine Learning (ML) models, provided their contextual knowledge about the classification problem thanks to the labeled data, constitute a promising approach towards mental health assessment. To promote the solution's trustworthiness, reliability, and accountability, explainability descriptions of the model's decision are provided in a graphical dashboard. Experimental results on a real dataset attain 90 % accuracy, improving those in the prior literature. The ultimate objective is to contribute in an accessible and scalable way before formal treatment occurs in the healthcare systems.
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