用多语言语言模型在社交媒体帖子中进行隐私保护抑郁症检测的联合学习
Samar Samir Khalil1,2, Noha S Tawfik1, Marco Spruit2,3
1Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt.
Patterns (New York, N.Y.)
|July 31, 2024
概括
联合学习 (FL) 通过多语言社交媒体数据有效检测抑郁症. 这种保护隐私的方法在不同的数据分布中保持了高性能,解决了心理健康研究中的关键挑战.
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
- 机器学习用于医疗保健
背景情况:
- 像抑郁症这样的精神健康状况的上升率需要早期检测策略.
- 通过自然语言处理 (NLP) 分析患者文本数据有希望,但面临隐私障碍.
- 联合学习 (FL) 提供了一个解决方案,通过使模型培训能够在不集中敏感患者数据的情况下实现.
研究的目的:
- 通过使用多语言社交媒体数据,评估联合学习 (FL) 模型用于抑郁症检测的有效性.
- 在不同的数据条件下评估FL性能,包括不同的语言和样本大小.
- 在模型培训过程中验证FL在保护客户隐私方面的能力.
主要方法:
- 利用模拟的多语言数据集,包括五种语言的社交媒体帖子.
- 应用联合学习 (FL) 技术用于训练抑郁症检测模型.
- 分析了不同客户端分区策略 (独立和非独立) 的模型性能.
主要成果:
- 联合学习 (FL) 在大多数测试场景中在抑郁症检测方面表现强.
- FL有效地维护了客户隐私,这是敏感健康数据的关键因素.
- 尽管客户之间语言和样本大小存在差异,但业绩仍然稳健.
结论:
- 联合学习 (FL) 是使用文本数据进行隐私保护心理健康分析的可行和有效方法.
- FL成功地平衡了对强大的模型性能需求与数据隐私的关键要求的需求.
- 这种方法具有很大的潜力,可以提前发现抑郁症等精神健康状况的早期发现.
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