基于多视图图的采访表示,以改善抑郁水平估计
Navneet Agarwal1, Gaël Dias2, Sonia Dollfus3,4,5
1UNICAEN, ENSICAEN, CNRS, GREYC, Normandie Univ, 14000, Caen, France. navneet.agarwal@unicaen.fr.
Brain informatics
|June 4, 2024
概括
这项研究引入了基于图形的表征,用于自动化抑郁症估计,比顺序模型表现出更高的性能. 这些方法为临床使用的访谈成绩单提供了新的见解.
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:
- 抑郁症的估计是至关重要的,大多数研究都集中在神经网络上.
- 抑郁症检测模型的替代输入表示尚未得到充分探索.
- 基于图形的方法为分析精神健康的文本数据提供了新的视角.
研究的目的:
- 调查基于图形的输入表示对抑郁症估计模型的影响.
- 将图形表示的效率与传统的顺序模型进行比较.
- 在抑郁症分析中引入多视图架构,考虑对话结构.
主要方法:
- 使用句子相似性和关键词相关性图表进行面试和语料库级别分析.
- 开发了多视图架构,单独处理问答对.
- 应用基于图形的编码对二进制分类任务进行度估计.
主要成果:
- 基于图形的表示表现显示出相对于顺序模型的显著优势.
- 在DAIC-WOZ二进制分类数据集上取得了新的最先进的结果.
- 多视图图形编码被证明对抑郁症检测有好处.
结论:
- 基于图形和多视图的方法为自动抑郁症估计提供了强大的替代方案.
- 这些方法为医疗专业人员提供了有价值的见解和视觉摘要.
- 该研究强调了新型数据表示在心理健康AI中的潜力.
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