用大模型自动分析临床访谈，辅助抑郁症评估
The MADRS Pipeline: Supporting Depression Assessment in Clinical Trials
- 将音频访谈转为文本，映射到十项MADRS症状
- 与专家评分相关性达0.867，可量化症状严重程度
- 适合临床试验场景，支持医生快速诊断
抑郁症是一种主要的精神障碍，其诊断主要依赖临床评估。通过精神病学MADRS量表实现自动化检测的方法日益受到关注。现有方法多集中于从网络文本、社交媒体等非结构化文本中识别抑郁，但在基于标准指南（如SIGMA）进行结构化访谈的临床试验中仍缺乏支持。本文开发了一种专为临床试验设计的LLM流程：将音频访谈转化为文本，将其映射至十项MADRS症状，评估症状严重程度，并识别异常评分。在真实临床访谈上的评估显示，该方法与专家评分整体相关性达0.867，为未来临床试验中的评估提供了可解释的支持。
原文摘要 · Abstract (English)
Depression is a major mental disorder for which diagnosis relies primarily on clinical assessments. Automated methods to support its detection via the psychiatric MADRS scale are getting more and more attention. While existing solutions primarily focus on detecting the disorder from different text sources (e.g., online text, social media), there is still limited support for clinical trials, where clinical assessments are conducted through structured interviews based on standard guidelines such as SIGMA. In this work, we develop a LLM pipeline specifically designed to support clinicians in supporting the assessment of depression in patients enrolled in clinical trials. Our pipeline converts audio interviews into transcripts, maps them into the ten MADRS symptom items, estimates their severity, and identify problematic clinical ratings associated with them. Evaluation on real clinical interviews shows a strong overall correlation of 0.867 with expert ratings, providing interpretable support for future assessments in clinical trials.
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