发表机构
Clario, part of Thermo Fisher Scientific（赛默飞世尔科技旗下Clario）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了一种 LLM 流程，可将临床试验的结构化音频访谈转换为文本，映射至 MADRS 10 项症状条目并评估严重程度，与专家评级整体相关性达 0.867，为抑郁评估提供可解释支持。
AI 中文摘要
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 量表支持抑郁症检测的自动化方法越来越受关注。现有解决方案主要聚焦于从不同文本来源（如网络文本、社交媒体）检测该障碍，而针对临床试验的支持仍有限，临床试验中基于 SIGMA 等标准指南通过结构化访谈开展临床评估。本研究开发了一种专为支持临床医生评估临床试验入组患者抑郁情况的 LLM 流程，该流程将音频访谈转换为文本，映射至 MADRS 的 10 项症状条目，评估其严重程度，并识别相关的问题临床评级。对真实临床访谈的评估显示，其与专家评级的整体相关性达 0.867，为临床试验的未来评估提供了可解释的支持。
英文摘要
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.
