MoodAngels用多智能体框架提升抑郁诊断准确率，兼顾隐私与临床有效性。
MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis
- 构建多智能体系统，分步验证临床评估，提升诊断准确性。
- 在真实病例上比GPT-4o高12.3%准确率，全系统表现更优。
- 开源1173例合成数据集，保护隐私同时保留真实统计特征。
AI在精神疾病诊断中面临主观性强、症状重叠及数据隐私限制等挑战。为此，我们提出首个专注于情绪障碍诊断的多智能体框架MoodAngels，结合细粒度临床评估分析与结构化验证流程，提升复杂精神数据的解读精度。同时，我们引入MoodSyn——一个包含1,173个合成精神病案例的开源数据集，在保持临床有效性的前提下确保患者隐私。实验表明，MoodAngels在真实病例上的基线智能体比GPT-4o高出12.3%准确率，完整多智能体系统进一步提升性能。在MoodSyn数据集上的评估显示，系统能精准复现原始数据的核心统计模式与复杂关系，同时具备强机器学习应用价值。两项贡献共同为计算精神病学提供先进诊断工具与关键研究资源。
原文摘要 · Abstract (English)
The application of AI in psychiatric diagnosis faces significant challenges, including the subjective nature of mental health assessments, symptom overlap across disorders, and privacy constraints limiting data availability. To address these issues, we present MoodAngels, the first specialized multi-agent framework for mood disorder diagnosis. Our approach combines granular-scale analysis of clinical assessments with a structured verification process, enabling more accurate interpretation of complex psychiatric data. Complementing this framework, we introduce MoodSyn, an open-source dataset of 1,173 synthetic psychiatric cases that preserves clinical validity while ensuring patient privacy. Experimental results demonstrate that MoodAngels outperforms conventional methods, with our baseline agent achieving 12.3% higher accuracy than GPT-4o on real-world cases, and our full multi-agent system delivering further improvements. Evaluation in the MoodSyn dataset demonstrates exceptional fidelity, accurately reproducing both the core statistical patterns and complex relationships present in the original data while maintaining strong utility for machine learning applications. Together, these contributions provide both an advanced diagnostic tool and a critical research resource for computational psychiatry, bridging important gaps in AI-assisted mental health assessment.
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