Large language model for interview-based depression diagnosis: an empirical study

The automatic diagnosis of depression plays a crucial role in preventing the deterioration of depression symptoms. The interview-based method is the most wildly adopted technique in depression diagnosis. However, the size of the collected conversation data is limited, and the sample distributions from different participants usually differ drastically. These factors present a great challenge in building a decent deep learning model for automatic depression diagnosis. Recently, large language models have demonstrated impressive capabilities and achieved human-level performance in various tasks under zero-shot and few-shot scenarios. This sheds new light on the development of AI solutions for domain-specific tasks with limited data. In this paper, we propose a two-stage approach that exploits the current most capable and cost-effective language model, ChatGPT, to make a depression diagnosis on interview-based data. Specifically, in the first stage, we use ChatGPT to summarize the raw dialogue sample, thereby facilitating the extraction of depression-related information. In the second stage, we use ChatGPT to classify the summarised data to predict the depressed state of the sample. Our method can achieve approximately 76% accuracy with a text-only modality on the DAIC-WOZ dataset. In addition, our method outperforms the performance of the state-of-the-art model by 6.2% in the D⁴ dataset. Our work highlights the potential of using large language models for diagnosis-based depression diagnosis.