使用多个实例学习的声学COVID-19检测
IEEE journal of biomedical and health informatics
|October 4, 2024
概括
一个机器学习工具分析咳和语音以检测COVID-19,提供了一个低成本,快速的诊断替代方案. 这种方法通过整合各种音频信号和患者数据,实现了92.2%的准确性.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
背景情况:
- 由于COVID-19的流行,需要快速和广泛的诊断测试.
- 传统的测试方法可能耗时且昂贵,限制了可访问性.
- 机器学习 (ML) 为低成本,可扩展的基于音频的诊断提供了一个潜在的解决方案.
研究的目的:
- 开发和评估基于ML的诊断工具,用于使用音频记录检测COVID-19.
- 通过DiCOVA挑战建立ML算法之间的可比性.
- 通过结合多种音频模式和患者元数据来提高诊断准确度.
主要方法:
- 使用了Coswara数据集,包括咳,言语,呼吸和母音发音记录.
- 采用在短音频间隔上预训练的基本ML模型.
- 实施了多个实例学习 (MIL) 模型,用于分析更长的记录.
- 应用了线性回归和其他融合方法,将DiCOVA挑战的融合类别的不同声音模式的预测结合起来.
主要成果:
- MIL方法提高了模型的通用性,在聚变类别中获得AUC ROC得分为86.6%.
- 结合持续的元音发音和患者元数据显著改善了性能.
- 最终的模型实现了92.2%的诊断准确度得分.
结论:
- 基于音频的ML分析是COVID-19查的一种有希望的,具有成本效益的方法.
- MIL方法有效地利用更长的音频录音来提高诊断准确度.
- 整合各种数据源,包括新的声音模式和元数据,进一步提高了ML诊断性能.
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