<p>Questions used in LLM prompting.</p>

Detecting depression from conversational text using large language models (LLMs) has garnered significant interest. However, the limited interpretability of existing methods presents a major challenge for clinical application. To address this, we propose a novel framework for automatic depression assessment, which employs LLM prompting to extract interpretable factors linked to depression from text and uses linear regression to predict severity scores. We evaluated our approach using a benchmark dataset (DAIC-WOZ; n = 186), predicting Patient Health Questionnaire (PHQ)-8 scores from clinical interview transcripts. Our method identifies key behavioral and linguistic features indicative of depression while also achieving state-of-the-art performance with a mean absolute error (MAE) of 2.91 on the test set. The resulting model further generalizes to an independent test dataset (E-DAIC; n = 86) with an MAE of 2.86. These findings suggest that interpretable LLM-based approaches hold significant promise for enhancing the clinical utility of automated depression assessment.借助大语言模型（Large Language Models，LLMs）从对话文本中检测抑郁症，已引发学界广泛关注。然而，现有方法可解释性不足的问题，为其临床应用带来了重大挑战。为此，我们提出了一种全新的自动化抑郁症评估框架：该框架通过大语言模型提示学习，从文本中提取与抑郁症相关的可解释性特征，并结合线性回归预测症状严重程度得分。我们采用基准数据集DAIC-WOZ（样本量n=186）对所提方法进行评估，从临床访谈转录文本中预测患者健康问卷（Patient Health Questionnaire，PHQ-8）得分。本方法不仅能够识别出表征抑郁症的关键行为与语言特征，还在测试集上以2.91的平均绝对误差（Mean Absolute Error，MAE）达成当前最优性能。所得到的模型可进一步泛化至独立测试数据集E-DAIC（样本量n=86），其MAE值为2.86。上述研究结果表明，基于大语言模型的可解释性方法，在提升自动化抑郁症评估的临床实用性方面具有可观的应用前景。