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Background <p>Depressive 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.</p> Methods <p>This 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.</p> Results <p>Evaluations 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.</p> Conclusions <p>This work highlights the importance of scalable data-generative pipelines for developing effective mental health LLM tools.</p>

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Enhanced large language models for effective screening of depression and anxiety

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • June M. Liu,
  • Mengxia Gao,
  • Sahand Sabour,
  • Zhuang Chen,
  • Minlie Huang,
  • Tatia M. C. Lee

摘要

Background

Depressive 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.

Methods

This 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.

Results

Evaluations 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.

Conclusions

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