用人工智能驱动的数据分析来预测喘风险
Meng-Han Chen1, Guanling Lee1, Lun-Ping Hung2
1Department of Computer Science and Information Engineering, National Dong Hua University, Hualien 974301, Taiwan.
Healthcare (Basel, Switzerland)
|April 12, 2025
概括
这项研究开发了一个使用语音分析来预测喘的AI系统. 机器学习模型实现了高精度,为临床医生提供了更快,非侵入性的诊断工具.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 免疫学 免疫学 免疫学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 喘是一种全球性呼吸系统和免疫系统疾病.
- 目前的喘诊断依赖于临床病史,体检和气流阻塞测试.
- 现有的诊断方法可能是侵入性的,耗时的,影响效率.
研究的目的:
- 开发一种基于人工智能的喘诊断预测系统.
- 为了利用与喘引起的呼吸努力相关的声音变化.
- 创建一个基于机器学习 (ML) 的临床决策支持工具.
主要方法:
- 分析了1500个语音样本 (高音,正常音,低音) 的音符[i,a,u].
- 使用长期平均频谱 (LTAS) 和单频过 Cepstral 系数 (SFCC) 提取的声学特征.
- 采用七个ML算法来评估喘预测可行性.
主要成果:
- 决策树,CNN和LSTM模型显示准确度高于0.8 (分别为0.88,0.80,0.84).
- 决策树在高音调语音分析方面表现出色;LSTM在正常和低音调语音方面表现最好.
- 特性重要性和光谱分析验证了模型的效率和可解释性.
结论:
- 人工智能驱动的声学分析可以显著提高喘诊断效率.
- 开发的系统为医学临床医生提供了准确可靠的决策支持.
- 这种方法为喘检测提供了一个非侵入性的替代方案.
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