Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation· 自我进化的人类中心框架用于抑郁症状的可解释标注
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
提出一种结合大语言模型与专家验证的自我进化抑郁症状标注框架。
- 核心方法
- 利用大语言模型辅助初步标注，再通过专家审核确认，实现与DSM-5-TR标准对齐的高质量数据集构建。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 解决抑郁症相关数据集标签缺乏结构化证据和症状层面解释的问题，提高数据透明度和下游模型的可解释性。
- 关键实验
- 未提供
- 主要贡献
- 提供了一个自我进化的专家参与的标注框架，支持构建更可靠和可解释的抑郁症数据集。
- 意义与局限
- 有助于提高抑郁症研究中AI系统的可靠性与可解释性，但不用于临床诊断。技术应用上仍有局限性。