统计和机器学习方法用于在特定人口地区的呼吸道疾病中识别生物标志物协会
Meshari Alazmi1,2, Amer AlGhadhban2,3, Abdulaziz Almalaq2,3
1College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
Frontiers in artificial intelligence
|December 15, 2025
概括
这项研究强调了用于诊断COVID-19和肺炎等呼吸道疾病的血液生物标志物. 机器学习模型使用这些生物标志物准确地分类条件,为非侵入性诊断铺平了道路.
科学领域:
- 生物化学 生物化学
- 医学诊断 医学诊断 医学诊断
- 计算生物学是一种计算生物学.
背景情况:
- 临床血液生物标志物对于非侵入性呼吸道疾病诊断越来越重要.
- 生物标志物驱动的方法为传统方法提供了具有成本效益,快速和可扩展的替代方案.
- 呼吸系统疾病的早期检测和预后受益于先进的诊断工具.
研究的目的:
- 评估15个血液生物标志物对四种呼吸系统疾病的诊断相关性.
- 通过使用统计和机器学习方法,识别疾病差异化的显著生物标志物相互作用.
- 评估机器学习模型对呼吸系统疾病分类的预测准确度.
主要方法:
- 从一个呼吸器诊所的913名患者的回顾性分析.
- 对15个血液生物标志物的统计相关性评估.
- 开发和应用决策树分类器 (机器学习) 来区分疾病.
主要成果:
- 确定了生物标志物之间显著的相关性,例如CRP和HGB (-55%),费里丁和LDH (+50%),以及红细胞与肌素/ESR之间的相关性.
- 机器学习模型实现了COVID-19 (F1-分数0.95),肺炎 (F1-分数0.85),喘 (F1-分数0.97) 和其他并发症 (F1-分数0.90) 的高预测准确度.
- 验证了综合生物标记面板和计算建模的诊断潜力.
结论:
- 血液生物标记面板显示了分类呼吸系统疾病的巨大潜力.
- 整合统计分析和机器学习可以提高诊断能力.
- 这种方法支持开发个性化,非侵入性和具有成本效益的呼吸道诊断工具.
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