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利用大型语言模型进行自动化抑郁查

Bazen Gashaw Teferra¹, Argyrios Perivolaris¹, Wei-Ni Hsiang²

¹Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada.

PLOS digital health

|July 28, 2025

概括

大型语言模型 (LLM) 通过分析临床采访数据,在查抑郁症方面表现有前途. 使用PHQ-8尺度,GPT模型在预测抑郁症状方面取得了最高的准确性.

科学领域:

人工智能在心理健康中的作用
自然语言处理应用程序
计算精神病学是一种计算精神病学.

背景情况:

心理健康诊断带来独特的管理挑战,影响健康和日常运作.
自我报告问卷是心理健康查的标准,但依赖于主观的,可能有偏见的答案.
通过自然语言处理量化自我报告的经验,尽管LLM取得了进展,但仍面临准确性的局限性.

研究的目的:

评估零射击学习的大型语言模型 (LLMs) 的有效性,用于选和评估使用项目尺度的抑郁症.
从临床访谈数据中证明LLM在预测自我报告问卷分数方面的潜力.

主要方法:

利用DAIC-WOZ数据集,这是一个公共资源,包含临床访谈成绩单和自我报告问卷数据.
使用RISEN提示工程框架来评估LLM对抑郁症状的预测能力 (PHQ-8项目).
根据精度和F1分数评估多个LLM,包括GPT模型,Llama3_8B,Cohere和Gemini.

主要成果:

在所有PHQ-8项目中,GPT模型,特别是GPT-4o,表现优于其他LLM (Llama3_8B,Cohere,Gemini),平均准确率为75.9%,F1得分为0.74.
GPT模型有效地预测了情绪和认知症状项目.
拉玛3_8B在检测无情症症状方面表现出色,而Cohere在识别精神运动活动症状方面表现出色.

结论:

通过从文字面试数据预测自我报告的问卷分数,LLM显示出有助于抑郁症查的巨大潜力.
初步的表现表明,LLM可以在心理健康保健中成为有价值的工具,尽管需要进一步的研究.
未来的工作重点应该是开发特定LLM应用精神健康症状的框架,并通过模型微调探索额外的数据集.

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