环境知识驱动的通用模型从音频录音中检测咳
Sudip Vhaduri1, Sayanton V Dibbo2, Yugyeong Kim3
1Department of Computer and Information TechnologyPurdue University West Lafayette IN 47907 USA.
概括
使用麦克风传感的机器学习模型可以通过分析咳来帮助诊断COVID-19等呼吸道疾病. 利用环境知识的引导模型比非引导方法准确度高出28%.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 呼吸系统疾病,包括COVID-19和喘,影响全球数百万人.
- 咳是一种常见的症状,需要客观地监测诊断和护理.
研究的目的:
- 开发和评估适应环境的机器学习模型,以客观报告咳症状.
- 评估麦克风传感器在帮助呼吸系统疾病诊断中的有用性.
主要方法:
- 开发了三种机器学习方法:无引导,半引导和引导.
- 模型被训练并使用多个数据集进行测试,包括开源的咳数据.
- 这些方法考虑了用户对声环境知识的不同水平.
主要成果:
- 与非引导模型相比,引导机器学习模型的准确性高达28%.
- 这些模型在各种数据集中显示了适用的性能,验证了它们的概括性.
- 准确度的提高与模型中纳入的环境知识程度相关.
结论:
- 导向机器学习模型在呼吸道疾病检测中为咳分析提供了卓越的性能.
- 导向模型的有效实施需要对周围的声环境有全面的了解.
- 这种方法有可能通过客观的症状评估来改善患者护理.
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