Generating Medically-Informed Explanations for Depression Detection using LLMs
This work addresses the need for explainable AI in mental health diagnostics, offering a novel method that enhances interpretability for clinicians and researchers, though it is incremental in combining existing techniques.
The paper tackled depression detection from social media by proposing LLM-MTD, a multi-task approach using a large language model to classify posts and generate medically-grounded explanations, achieving state-of-the-art performance with significant improvements in AUPRC on the RSDD dataset.
Early detection of depression from social media data offers a valuable opportunity for timely intervention. However, this task poses significant challenges, requiring both professional medical knowledge and the development of accurate and explainable models. In this paper, we propose LLM-MTD (Large Language Model for Multi-Task Depression Detection), a novel approach that leverages a pre-trained large language model to simultaneously classify social media posts for depression and generate textual explanations grounded in medical diagnostic criteria. We train our model using a multi-task learning framework with a combined loss function that optimizes both classification accuracy and explanation quality. We evaluate LLM-MTD on the benchmark Reddit Self-Reported Depression Dataset (RSDD) and compare its performance against several competitive baseline methods, including traditional machine learning and fine-tuned BERT. Our experimental results demonstrate that LLM-MTD achieves state-of-the-art performance in depression detection, showing significant improvements in AUPRC and other key metrics. Furthermore, human evaluation of the generated explanations reveals their relevance, completeness, and medical accuracy, highlighting the enhanced interpretability of our approach. This work contributes a novel methodology for depression detection that combines the power of large language models with the crucial aspect of explainability.