TAME 疼痛数据发布:使用音频信号来描述疼痛
Tu-Quyen Dao1, Eike Schneiders2,3, Jennifer Williams3
1University of Texas at Austin, Department of Molecular Biosciences, Austin, 78712, US. tuquyendao@austin.utexas.edu.
Scientific data
|April 10, 2025
概括
这项研究介绍了TAME疼痛数据集,这是诱导疼痛期间最大的语音数据集. 它使人工智能开发用于非侵入性疼痛评估,改善患者护理.
科学领域:
- 医学科学 医学科学 医学科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 准确的疼痛评估对于患者护理至关重要,但仍然具有挑战性.
- 目前用于疼痛评估的方法在临床实践中存在局限性.
- 语音分析提供了一种潜在的非侵入性方法来检测疼痛.
研究的目的:
- 介绍TAME疼痛数据集,这是研究诱导的急性疼痛和成年人的言语之间的关系的新资源.
- 支持人工智能驱动技术的开发,以客观评估疼痛.
- 促进疼痛管理和医疗保健领域的跨学科研究.
主要方法:
- 利用冷压力任务 (CPT) 诱导51名成年参与者的急性疼痛.
- 记录了来自参与者报告他们的疼痛水平的7,000多个声音发言.
- 收集了包括背景噪音,语音错误和非语音声音特征在内的全面注释.
主要成果:
- TAME疼痛数据集是最大的公开可用的数据集,它将诱导的疼痛与语音特征相关联.
- 数据包括用于高级音频分析和机器学习模型开发的详细注释.
- 建立了与自我报告的疼痛强度相关联的声音线索的基础.
结论:
- TAME疼痛数据集是推动非侵入性疼痛评估技术的宝贵资源.
- 促进人工智能,心理学和医学科学的研究,以改善疼痛管理.
- 对远程医疗和远程医疗机构有重大影响,增强了患者监测和护理.
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