改进了大型语言模型,以有效选抑郁和焦虑
June M Liu1, Mengxia Gao1,2, Sahand Sabour3
1Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, China.
Communications medicine
|November 5, 2025
概括
一个名为EmoScan的新系统使用Large Language Models (LLMs) 来选焦虑和抑郁等情绪障碍. 它展示了卓越的表现和面试技巧,为可扩展的心理健康工具铺平了道路.
科学领域:
- 人工智能的人工智能
- 临床心理学 临床心理学
- 计算语言学 计算语言学
背景情况:
- 抑郁症和焦虑症是普遍存在的全球健康问题,需要早期发现和干预.
- 未经治疗的心理健康状况会导致严重的后果,包括自杀和重大的社会经济成本.
- 大型语言模型 (LLM) 对心理健康应用有希望,但在成本和数据伦理方面面临挑战.
研究的目的:
- 介绍PsyInterview,一个用于生成合成临床访谈数据的管道.
- 介绍EmoScan,一种基于LLM的系统,用于选情绪障碍.
- 评估EmoScan在诊断粗和细粒度情绪障碍方面的有效性及其采访能力.
主要方法:
- 开发了一个数据合成管道,以创建1,157个交互式临床对话 (PsyInterview).
- 构建了EmoScan,一种基于LLM的系统,能够区分各种情绪障碍.
- 使用自动化和人类评估来评估EmoScan的选准确性,解释质量,概括性和面试技巧.
主要成果:
- 在选情绪障碍方面,EmoScan获得了高F1得分 (0.7467),表现优于基准模型和GPT-4.
- 该系统表现出卓越的解释能力 (BERTScore=0.9408) 和强大的通用性 (F1-score=0.67对外部数据).
- 通过自动化和人类评估验证了EmoScan的采访技巧,超越了基线模型.
结论:
- 可扩展的数据生成管道对于开发有效的基于LLM的心理健康工具至关重要.
- 情感扫描代表了人工智能驱动的情绪障碍查的重大进步.
- 这项研究强调了LLM在改善心理健康护理的可访问性和效率方面的潜力.
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