对Helicobacter pylori感染和外周动脉样硬化之间的关系进行可解释的机器学习分析:一个回顾性队列研究
Chenlu Li1, Qingshi Lin2, Liaoliao Yang1
1Department of Gastroenterology, Affiliated Yueqing Hospital, Wenzhou Medical University, Wenzhou, China.
Frontiers in cellular and infection microbiology
|February 27, 2026
概括
杆菌感染是外周动脉样硬化 (PA) 的重要独立危险因素. 机器学习模型证实了H. pylori的存在.
科学领域:
- 心血管研究研究心血管研究
- 传染性疾病 传染性疾病
- 生物统计学 生物统计学
背景情况:
- 杆菌 (H. pylori) 感染是导致外周动脉样硬化 (PA) 的潜在因素.
- 在大量人群中,H. pylori对PA风险的预测价值需要进一步调查.
研究的目的:
- 评估H. pylori感染对PA风险的预测性贡献.
- 用可解释机器学习 (ML) 来评估H. pylori与传统临床因素结合的作用.
主要方法:
- 追溯分析了5,862个人的健康检查数据.
- 使用后勤回归,子组分析,受限立方线 (RCS) 和14个ML算法.
- 采用了SHapley添加式解释 (SHAP) 来解释特征,并进行了纵向和调解分析.
主要成果:
- 甲状腺菌感染显示出与PA有强烈的独立关联 (OR=5.27).
- ML模型显示了高预测性能 (AUC=0.993与所有变量).
- SHAP分析确定了H. pylori作为主要预测因素;纵向数据显示新出现的PA病例中持续感染.
结论:
- 感染H. pylori是一种独立的风险因素,对PA风险分层至关重要.
- 将流行病学数据与可解释的ML集成为识别高风险个体提供了一种可靠的方法.
- 传染性标记物,如H. pylori,可能会增强血管风险评估策略.
关键词:
在Helicobacter Pylori的研究中,沙普利的添加剂扩张分析分析.机器学习是机器学习.调解分析 调解分析周围动脉样硬化症 (Peripheral Atherosclerosis) 是一个疾病.更多相关视频
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