Enhanced large language models for effective screening of depression and anxiety.

backgroundDepressive and anxiety disorders are widespread, necessitating timely identification and management. These conditions manifest through various emotional and behavioral symptoms, such as persistent sadness and excessive worry. When left undiagnosed and untreated, these disorders can cause severe consequences, including increased risk of suicide and substantial socioeconomic burden. Recent advances in Large Language Models (LLMs) offer potential solutions, yet high costs and ethical concerns about training data remain challenges.

methodsThis paper introduces a pipeline for synthesizing clinical interviews, resulting in 1,157 interactive dialogues (PsyInterview), and presents EmoScan, an LLM-based emotional disorder screening system. EmoScan distinguishes between coarse (e.g., anxiety or depressive disorders) and fine disorders (e.g., major depressive disorders) and conducts high-quality interviews.

resultsEvaluations show that EmoScan exceeds the performance of base models and other LLMs like GPT-4 in screening emotional disorders (F1-score = 0.7467). It also delivers superior explanations (BERTScore=0.9408) and demonstrates robust generalizability (F1-score of 0.67 on an external dataset). Furthermore, EmoScan outperforms baselines in interviewing skills, as validated by automated ratings and human evaluations.

conclusionsThis work highlights the importance of scalable data-generative pipelines for developing effective mental health LLM tools.