用于可解释抑郁症症状标注的自我进化以人为中心框架
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
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中文总结 AI 辅助
针对抑郁症标注质量瓶颈，提出结合大语言模型辅助与专家验证的自我进化标注框架，分三阶段运行，采用双记忆架构，能提高标注一致性和可解释性，减少人工修订，还输出多种信息实现可审计性。
中文摘要 AI 辅助
标注质量是构建用于心理健康研究的可靠且可解释的人工智能（XAI）系统的主要瓶颈。在抑郁症相关数据集中，标签分配往往缺乏结构化证据、症状级别的依据，或与《精神疾病诊断与统计手册》第五版修订版（DSM - 5 - TR）标准的可追溯对齐，限制了透明度和下游模型的可解释性。我们提出了一种针对重度抑郁症（MDD）的自我进化、专家参与的标注框架，它将大语言模型（LLM）辅助标注与专家验证相结合。该框架旨在支持构建与DSM - 5 - TR对齐的可解释数据集，而非进行临床诊断。它分三个阶段运行：从文本记录中选择候选证据、进行DSM - 5 - TR标准级分析以及进行案例级综合以生成标签级诊断和严重程度标注。一种由示例记忆和反思记忆组成的双记忆架构旨在内化专家反馈并在无需重新训练的情况下迭代改进未来标注。除最终标签外，该框架还输出临床证据、推理痕迹和编辑历史，实现全面的可审计性。在一项使用专家评审样本的试点研究中，该方法提高了标注的一致性和可解释性，同时减少了人工修订工作量。
英文摘要
Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability. We propose a self-evolving, expert-in-the-loop annotation framework for Major Depressive Disorder (MDD) that combines large language model (LLM)-assisted labeling with expert verification. The framework is intended to support the construction of explainable, DSM-5-TR-aligned datasets rather than to perform clinical diagnosis. It operates in three stages: candidate evidence selection from textual records, criterion-level DSM-5-TR analysis, and case-level synthesis that produces label-level diagnostic and severity annotations. A dual-memory architecture, composed of Example Memory and Reflection Memory, is designed to internalize expert feedback and iteratively improve future annotations without retraining. We describe this mechanism and leave its evaluation across multiple feedback cycles to future work. In addition to final labels, the framework exports clinical evidence, reasoning traces, and edit histories, enabling comprehensive auditability. In a pilot study using expert-reviewed samples, the proposed approach improves annotation consistency and explainability while reducing manual revision effort.
