通过使用语言模型检测网络论坛用户的抑郁症,实现早期医疗保健干预:纵向分析和评估
David Owen1, Dimosthenis Antypas1, Athanasios Hassoulas2
1School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom.
JMIR AI
|August 1, 2023
概括
机器学习模型,特别是基于变压器的语言模型 (LMs),可以识别社交媒体用户的抑郁症. 关键的语言模式在诊断前3-4个月出现,使得早期心理健康干预成为可能.
科学领域:
- 计算语言学计算语言学
- 精神病学是一个精神病学.
- 机器学习是机器学习.
背景情况:
- 大型抑郁症影响全球5%的成年人,需要早期诊断和干预.
- 抑郁症的认知扭曲在书面语言中表现出来,为基于文本的分析提供了潜力.
- 社交媒体和网络论坛为早期发现抑郁症提供了丰富的数据.
研究的目的:
- 评估机器学习 (ML) 方法来识别有抑郁症诊断的Reddit用户.
- 通过文本分析来确定诊断后时间在检测抑郁症方面的相关性.
主要方法:
- 利用了两个Reddit数据集,包括来自有抑郁症诊断和没有抑郁症诊断的用户的帖子.
- 应用了ML分类器,包括基于变压器的语言模型 (LMs),以分析用户帖子.
- 估计抑郁症诊断日期用于分析用户沟通中的时间模式.
主要成果:
- 基于变压器的LM,特别是BERT和MentalBERT,在对患有抑郁症的用户进行分类方面取得了最高的有效性 (平均F1得分为0.64).
- 在估计诊断日期之前的12-16周显示了抑郁症最有指示性的语言模式.
- 在诊断前4-8周的帖子显示出明显更多的负面情绪.
结论:
- 基于变压器的LM显示出在社交媒体平台上识别有风险的个人的前景.
- 这些模型分析的语言特征可以在几周到几个月之前出现抑郁症.
- 这种能力可以为处于危险状态的个人提供积极的心理健康护理干预措施.
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