将临床焦虑量表与预训练的语言模型集成在一起,用于在社交媒体上识别焦虑
Jianghong Zhu1, Zhenwen Zhang1, Zepeng Li1
1Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000 China.
Health information science and systems
|September 9, 2025
概括
这项研究引入了一种新的框架,用于使用语言模型和临床尺度来识别来自社交媒体的焦虑. 该方法通过分析用户帖子中的语义相似性来增强早期焦虑检测.
科学领域:
- 计算语言学 计算语言学
- 心理学 心理学 心理学
- 数字健康数字健康
背景情况:
- 对心理健康的社交媒体分析正在增长,但缺乏深刻的语义理解和临床整合.
- 目前从社交媒体上识别焦虑的模型往往缺乏可解释性和医学相关性,因为领域知识有限.
研究的目的:
- 通过将预先训练的语言模型与临床焦虑量表集成,开发一个先进的社交媒体焦虑识别框架.
- 改进用户级语义建模和焦虑特征提取,以更准确地监测心理健康.
- 提高焦虑识别模型的可解释性和医学相关性.
主要方法:
- 利用预训练的语言模型从社交媒体帖子和临床焦虑量表中提取语义表示.
- 根据用户帖子和焦虑量表之间的语义相似性计算焦虑分数.
- 实施了一项联合培训策略,涉及序列和文本分类,以实现强大的焦虑识别.
- 构建了一个大规模数据集 (DAUR) 和一个子集 (DAUR_PRE) 用于早期检测焦虑.
主要成果:
- 拟议的框架在DAUR和DAUR_PRE数据集上表现出高于现有方法的性能.
- 实现了高分类准确度,特别是在DAUR_PRE.上早期焦虑识别的具有挑战性的场景中.
- 临床尺度的整合提高了模型识别细微焦虑状态的能力.
结论:
- 这种新的框架通过将深度语义建模与临床见解相结合,有效地识别来自社交媒体的焦虑状态.
- 这种方法在早期发现焦虑和个性化心理健康监测方面显示出显著的前景.
- 未来的研究可以在这个框架的基础上开发更复杂和经过临床验证的数字心理健康工具.
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