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一个机器学习模型用于识别慢性阻塞性肺病的症状患者的并发性血管疾病

Yufeng Gu¹, Ping Chen², Shuhong Wang³

¹Department of Information, Suining Central Hospital, Suining, China.

Annals of medicine

|November 21, 2025

概括

一个新的机器学习模型有效地识别慢性阻塞性肺病 (COPD) 患者的血管疾病. 这种工具有助于更好地诊断并减少COPD与血管疾病的不必要干预.

科学领域:

肺部医学肺部医学
心血管医学心血管医学
医疗保健中的人工智能

背景情况:

慢性阻塞性肺病 (COPD) 经常与血管疾病有关,这会对患者的预后和生活质量产生负面影响.
在有症状的COPD患者中识别并发性血管疾病对于有效管理至关重要.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于早期识别患有症状性COPD的患者的并发性血管疾病.
在这个特定的患者群体中评估各种ML算法的临床实用性和性能.

主要方法:

对6274名COPD患者的数据进行了回顾性分析.
使用LASSO回归进行特征选择,然后应用八种ML算法,包括后勤回归,随机森林,梯度增强,SVM,神经网络,CNN,AdaBoost和堆叠.
使用AUC,校准曲线和决策曲线分析 (DCA) 的模型性能评估.

主要成果:

堆叠模型表现出最高的性能,AUC为0.867,准确度为79.4%,灵敏度为74.9%,特异性为84.0%.
堆叠模型表现出优异的校准,并在DCA中提供了最高的净临床益处.
在0.2的值,该模型可以防止~35%的不必要的干预,并识别~75%的高风险患者.

结论:

开发的堆叠ML模型是识别有症状的COPD患者并发性血管疾病的卓越工具.
该模型具有强大的区分能力,校准和临床实用性,作为有效的决策支持工具.
这种方法可以优化高风险的COPD患者的诊断评估,这些患者有潜在的血管并发症.

关键词:

机器学习就是机器学习.慢性阻塞性肺病慢性阻塞性肺病预测模型预测模型这是一项回顾性研究.血管疾病是血管疾病.

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