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人类咳注释器的性能评估:最佳指标和性别差异
Isabel Sanchez-Olivieri1, Matthew Rudd2, Juan Carlos Gabaldon-Figueira3
1Universidad de Navarra, Pamplona, Spain.
BMJ open respiratory research
|November 9, 2023
概括
量化咳是一个挑战. 使用"咳秒"进行注释可以提高准确性并减少差异,显示注释者之间的良好协议,对于开发自动咳监测系统至关重要.
科学领域:
- 医疗器械 医疗器械
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 缺乏准确的咳量化,阻碍了自动化监测系统的发展.
- 人类注释是黄金标准,但需要可复制的方法.
- 新兴的自动化技术需要验证的注释方法.
研究的目的:
- 为了评估咳声音 (内标签和内标签) 的人类注释器协议.
- 为了比较使用咳计数与咳秒的注释协议.
- 为了确定咳特征中的潜在的性别差异.
主要方法:
- 24名参与者戴着录音智能手表6-24小时.
- 收集了400个小时的音频;分析了40个小时.
- 使用线性和布兰德-阿尔特曼分析来比较咳计数和咳秒.
主要成果:
- 取得了优异的内部标签 (皮尔森r=0.98) 和内部标签 (皮尔森r=0.96) 协议.
- 与咳计数相比,使用咳秒减少了50%的注释器差异.
- 在咳声音长度和时代大小方面观察到显著的性别差异.
结论:
- 咳秒被提出为人工咳注释的更可靠单位,减少了变化.
- 这种方法对于评估自动咳监测系统至关重要.
- 观察到的性别差异凸显出需要公平地开发咳监测工具.
关键词:
咳 / 机制 / 药理学
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