在中国的初级诊所中,通过机器学习和有限的实验室参数,以数据驱动的快速检测Helicobacter pylori感染
Shiben Zhu1, Xinyi Tan2,3,4, He Huang2,3,4
1School of Nursing and Health Studies, Hong Kong Metropolitan University, Kowloon, Hong Kong, SAR, 999077, China.
Heliyon
|August 22, 2024
概括
机器学习模型可以使用常规血液检测检测Helicobacter pylori (H. pylori) 感染. 这种方法有助于初级诊所进行诊断和查,减少对侵入性方法的依赖.
科学领域:
- 医疗信息学 医疗信息学
- 临床诊断 临床诊断 临床诊断
- 医疗保健中的机器学习
背景情况:
- 杆菌 (H. pylori) 感染是全球胃癌的主要危险因素.
- 当前的诊断方法在初级保健机构中往往是不可访问的,侵入性的或不准确的.
研究的目的:
- 开发和验证机器学习 (ML) 模型,以使用常规血液检测参数检测H. pylori感染.
- 确定与H. pylori感染相关的关键血液生物标志物和临床结果.
主要方法:
- 追溯分析了1409名成年患者的数据,包括23个血液测试参数.
- 三个ML和五个组合模型的应用,用尿素呼吸测试作为黄金标准.
- 利用随机森林 (RF) 模型与特征重要性和SHAP分析用于生物标志物识别.
主要成果:
- 随机森林 (RF) 模型在没有特征选择的情况下实现了高性能 (ROC=0.951,精度=0.894).
- 确定的主要生物标志物包括白细胞计数 (WBC),平均血小板体积 (MPV),血红蛋白 (Hb),红细胞计数 (RBC),血小板评分 (PCT) 和血小板计数 (PLC).
- 这些生物标志物与H. pylori感染有显著的相关性,表明免疫反应和炎症.
结论:
- 常规血液检测异常可能表明H. pylori感染,促使进一步调查.
- 射频ML模型提供了一种可行的,非侵入性的方法,用于在初级诊所检测H. pylori.
- 这种方法可以提高诊断和查效率,降低医疗保健负担.
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