基于机器学习的诊断模型,用于慢性阻塞性肺部疾病的急性恶化,使用常规临床实验室参数
Youpeng Chen1,2,3, Yabang Chen4, Junquan Sun4
1Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Annals of the New York Academy of Sciences
|October 22, 2025
概括
机器学习模型现在可以客观地使用常规实验室测试来诊断慢性阻塞性肺病 (AECOPD) 的急性恶化. 一个由九个变量组成的模型显示出高准确度,有助于在各种医疗保健环境中做出临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 肺部病理学 肺部病理学
- 机器学习 机器学习
背景情况:
- 慢性阻塞性肺病 (AECOPD) 的急性恶化显著增加了患者的发病率和医疗费用.
- 目前AECOPD的诊断标准缺乏客观性,导致治疗的潜在延迟或不准确.
研究的目的:
- 开发和验证用于使用常规实验室参数客观诊断AECOPD的机器学习模型.
- 确定一组节的实验室变量,可以有效地区分AECOPD和稳定的COPD.
主要方法:
- 对25965名COPD患者病历进行了回顾性分析,数据分为培训和测试队列.
- 评估了113种机器学习模型组合,重点是诊断性能指标,如AUC,校准曲线和决策曲线分析.
- 一个通用的线性模型增强 + 随机森林 (glmBoost + RF) 模型被开发和优化.
主要成果:
- 优化的glmBoost + RF模型仅使用九个常规实验室变量实现了优异的诊断性能 (训练AUC = 0.993,测试AUC = 0.834).
- 九变量模型表现出与更复杂的48变量模型相比的性能,提供了更好的临床适用性.
- 两种开发的模型都表现出强大的校准和在不同的人口群体中一致的性能.
结论:
- 使用九个常规临床实验室参数的机器学习模型可以有效地区分AECOPD和稳定的COPD.
- 这为AECOPD提供了一个有价值,客观的诊断工具,特别有利于资源有限的医疗保健机构.
- 开发的模型增强了肺病学和医疗信息学中的诊断能力.
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