一项可解释机器学习的基线研究,使用GC-MS呼吸VOC来分类喘,支气管切除和COPD
Eun-Ji Ko1, Si-On Bae1, Daesung Kang2
1School of Bio-Health Convergence, College of Natural Sciences, Sungshin Women's University, Seoul, Republic of Korea.
Scientific reports
|December 23, 2025
概括
精确区分呼吸道疾病,如喘,支气管切除症和COPD是具有挑战性的. 这项研究使用呼吸分析 (呼吸学) 和人工智能以高准确度分类这些疾病,识别关键生物标志物.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 由于症状重叠,精确诊断喘,支气管切除和COPD在临床上具有挑战性.
- 传统的诊断工具在区分这些呼吸道疾病方面存在局限性.
- 呼吸分析 (呼吸学) 提供了一种非侵入性的疾病检测方法.
研究的目的:
- 建立一种可复制的方法来分类喘,支气管炎和COPD,使用呼出的气息挥发性有机化合物 (VOC).
- 为了评估监督机器学习分类器对呼吸系统疾病分类的性能.
- 使用可解释的人工智能识别与每个疾病相关的关键VOC生物标志物.
主要方法:
- 利用了121个呼吸样本和76个VOC的公开数据集.
- 采用气色谱-质谱法 (GC-MS) 进行呼吸样本分析.
- 在嵌套交叉验证下评估了七个监督分类器,其中XGBoost表现最佳.
- 应用沙普利添加剂解释 (SHAP) 用于生物标志物识别.
主要成果:
- XGBoost分类器的平均精度为95.83%,宏观平均AUC为0.998.8%.
- 确定了具有影响力的VOC,包括2-基和六甲,作为潜在的生物标志物.
- 确定的VOCs的疾病特异性和跨疾病相关性已被证明.
结论:
- 呼吸学与可解释的AI相结合,为呼吸系统疾病分类提供了一个可扩展的,非侵入性的工具.
- 这项研究为呼吸道疾病的呼吸学研究建立了可重现的基线.
- 已识别的VOC可以作为未来临床验证和诊断开发的候选生物标志物.
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