在社交媒体上适应语言模型进行心理健康分析
Mario Ezra Aragón1, Adrián Pastor López-Monroy2, Manuel Montes-Y-Gómez3
1Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Rúa Jenaro de la Fuente, Santiago de Compostela, 15782, A Coruña, Spain.
Artificial intelligence in medicine
|July 20, 2025
概括
这项研究引入了一个新的框架,用于调整语言模型以检测精神健康障碍,如厌食症,抑郁症,博和社交媒体上的自我伤害. 这些模型在早期风险检测和分类任务中表现有前途.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 社交媒体分析.
背景情况:
- 精神障碍显著影响全球认知和行为功能.
- 越来越多的人对利用社交媒体来识别心理健康障碍越来越感兴趣.
- 需要强大的模型来分析各种心理健康状况.
研究的目的:
- 开发和评估语言模型的域适应框架,以在社交媒体上识别四种精神障碍 (厌食症,抑郁症,博,自我伤害).
- 为了比较不同语言模型在内部和跨域设置中的性能,用于心理健康分析.
- 用临床工具评估模型的预测能力,并评估适应方法的有效性.
主要方法:
- 一个三个阶段的语言模型适应策略:数据丰富,社交媒体语言适应和多任务微调.
- 收集和丰富四种心理障碍特有的大量数据.
- 采用适配器用于对分类任务的微调模型,并进行跨领域的比较研究.
主要成果:
- 开发的心理健康模型在早期风险检测方面表现强.
- 与基线模型相比,适应框架提高了分类性能.
- 模型在域内和跨域检测场景中都显示出有效性.
结论:
- 拟议的领域适应框架对于在社交媒体上识别心理健康障碍是强大而有效的.
- 这项研究为早期风险检测和心理健康状况的分类提供了有希望的方法.
- 这些发现表明,适应的语言模型可以对心理健康信息学做出重大贡献.
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