装配变压器,对抑郁症情绪和严重性检测进行后期解释
Sazzadul Islam1, Rezaul Haque2, Mahbub Alam Khan3
1Department of Computer Science and Engineering, BRAC University, Dhaka 1212, Bangladesh.
iScience
|February 3, 2026
概括
这项研究引入了一个AI框架,使用集合变换器来检测社交媒体文本中的抑郁情绪和严重程度,提高心理健康支持的准确性和可解释性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 心理健康支持需要可扩展和可靠的AI解决方案.
- 社交媒体文本包含与抑郁相关的情绪和严重程度的有价值指标.
- 现有的方法在抑郁症检测中难以准确和可解释.
研究的目的:
- 开发一个集成变压器框架 (DepTformer-XAI-SV) 来检测与抑郁症相关的情绪,并在社交媒体中分类严重程度.
- 通过加权软投票机制和LIME (本地可解释模型-不可知论解释) 来提高模型的准确性和可解释性.
- 为应对心理健康数据集固有的阶级不平衡挑战.
主要方法:
- 将四个变压器模型集成到一个整体框架中.
- 基于验证宏观F1分数的加权软投票机制,以提高准确性.
- 利用LIME进行解释,突出重点语言特征.
- 采用阶级加权交叉,分层k倍分割和少数意识的抽样来处理阶级不平衡.
- 对两个基准数据集的评估:DepressionEmo和合并的抑郁症严重性检测 (MDSD).
主要成果:
- 在DepressionEmo上,DepTformer-XAI-SV模型取得了80.44%的宏F1得分,在MDSD上达到79.88%的得分.
- 整体框架显著优于单个变压器模型和传统方法.
- 在检测少数群体阶级方面取得了实质性的改进,这对于不平衡的数据集至关重要.
- LIME成功地识别了有助于抑郁症检测的关键语言特征.
结论:
- 拟议的整体变压器框架为社交媒体中抑郁症检测和严重程度分类提供了可扩展和准确的解决方案.
- 整合XAI (可解释AI) 技术提高了对心理健康AI应用程序的信任和可解释性.
- 开发了一款用于交互和可解释推断的网络应用程序,以促进实际应用.
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