一个多模式抑郁症咨询语音和文本数据集与HAMD-17评估
Pengfei Cao1,2, Yuanzhe Zhang3, Chenxiang Zhang1
1The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Scientific data
|September 29, 2025
概括
创建了一个新的抑郁症咨询 (PDCH) 数据集,用于训练人工智能 (AI) 模型. 这一多式联络数据集解决了心理健康专业人员的短缺问题,并改善了对抑郁症的AI诊断.
科学领域:
- 心理健康 心理健康
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 全球抑郁症的发病率正在上升,特别是在中国,这对公众健康构成了重大挑战.
- 心理健康专业人员严重短缺,需要创新的解决方案.
- 人工智能 (AI),特别是大型语言模型,显示了改善心理健康诊断的潜力.
研究的目的:
- 为了解决缺乏高质量的现实数据,用于培训和评估心理健康中的AI模型.
- 引入一种新的多式联络数据集,用于抑郁症咨询分析.
- 促进开发更准确的AI驱动的抑郁症诊断工具.
主要方法:
- 从北京安丁医院的临床咨询中构建一个多式联络数据集 (PDCH).
- 包括音频录音,转录文本和汉密尔顿抑郁症评分表 (HAMD-17) 评分.
- 数据集包括100次咨询,超过2937分钟的音频,有详细的对话.
主要成果:
- PDCH数据集为AI模型开发提供了丰富的现实世界临床数据来源.
- 通过音频,文本和临床尺度对抑郁症咨询进行全面分析.
- 能够更准确地评估抑郁症的人工智能模型的创建.
结论:
- 该PDCH数据集弥合了人工智能研究心理健康数据可用性的差距.
- 它支持人工智能在精神卫生保健领域的发展,特别是在诊断方面.
- 这种资源对于开发更有效,更容易获得的人工智能驱动的心理健康解决方案至关重要.
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