用第一人称代词的深度表示来预测抑郁症症状严重程度
Xinyang Ren1, Hannah A Burkhardt1, Patricia A Areán2
1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
分析人们在治疗文本中如何使用"我"和"我",使用先进的人工智能,可以更好地预测抑郁症的严重程度,而不仅仅是计算它们. 这通过语言分析提供了对心理健康的新见解.
科学领域:
- 自然语言处理自然语言处理.
- 计算精神病学是一种计算精神病学.
- 心理语言学 心理语言学
背景情况:
- 对文本中第一人称单数代词 (例如"我"",我") 的分析与心理状态,特别是抑郁症严重程度有关.
- 代词的传统频率计数忽略了微妙的使用模式.
- 神经语言建模的进步为通过上下文嵌入来分析语言提供了新的方法.
研究的目的:
- 利用第一人称单数代词的上下文嵌入来分析心理状态.
- 与传统方法相比,调查语境代词使用分析是否可以改善抑郁症症状严重程度的预测.
主要方法:
- 利用了从在线心理治疗课程中删除身份的短信.
- 采用语境化语言表示模型来生成第一人称单数代词的嵌入.
- 对比语境代词嵌入的预测性能与标准代码嵌入和基于频率的分析.
主要成果:
- 语境化第一人称代词嵌入在预测抑郁症症状严重程度方面表现出卓越的表现.
- 这种方法的性能优于标准分类令牌嵌入和简单的频率计数.
- 这些发现强调了代词使用背景在语言分析中对心理健康的重要性.
结论:
- 第一人称单数代词的上下文表示为抑郁症症状严重程度提供了增强的预测实用性.
- 这种方法提供了对抑郁症患者语言使用的更细致的理解.
- 利用先进的NLP技术可以改善心理健康评估的计算方法.
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