This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment using the Montgomery-Asberg Depression Rating Scale (MADRS). We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews from the Context-Adaptive Multimodal Informatics (CAMI) dataset, demonstrates strong correlations with clinician assessments. The Qwen 2.5--72b model achieves near-human level agreement across most MADRS items, with Intraclass Correlation Coefficients (ICC) closely approaching those between human raters. We provide a comprehensive analysis of model performance across different MADRS items, highlighting strengths and current limitations. Our findings suggest that LLMs, with appropriate prompting, can serve as efficient tools for mental health assessment, potentially increasing accessibility in resource-limited settings. However, challenges remain, particularly in assessing symptoms that rely on non-verbal cues, underscoring the need for multimodal approaches in future work.

本研究提出LlaMADRS框架，利用开源大型语言模型自动化抑郁严重程度评估，解决了传统评估方法的效率问题。通过零-shot 提示策略，该方法在真实访谈中展现出与临床评估的强相关性，表明大型语言模型在心理健康评估中的潜力，尤其在资源有限的环境中。然而，对于依赖非语言线索的症状评估仍面临挑战，未来需要多模态的方法。

LlaMADRS：利用大型语言模型进行基于访谈的抑郁评估