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.
Lee et al. (Mon,) studied this question.