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一种机器学习模型用于预测在家治疗慢性阻塞性肺病患者的急性恶化

Huiming Yin¹, Kun Wang², Ruyu Yang¹

¹Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital, Hunan University of Medicine, Huaihua 418000, China.

Computer methods and programs in biomedicine

|February 14, 2024

概括

机器学习模型使用远程监测数据准确预测COPD (AECOPD) 的急性恶化. CatBoost 模型利用电子听筒数据,在预测 AECOPD 事件方面表现出卓越的表现.

科学领域:

肺部医学肺部医学
医疗保健中的人工智能
生物医学工程生物医学工程

背景情况:

慢性阻塞性肺病 (COPD) 的管理需要持续监测急性恶化 (AECOPD).
使用智能设备进行远程监控为COPD患者的管理提供了一个有前途的方法,特别是那些在家中的患者.

研究的目的:

开发和评估机器学习 (ML) 模型,用于预测COPD组C/D患者的AECOPD事件.
评估ML模型的有效性,使用便携式螺旋计和电子耳语镜的数据进行远程患者监测.

主要方法:

招募了66名COPD组C/D的患者,根据GOLD 2018指南进行诊断.
开发了XGBoost,LightGBM和CatBoost ML模型,使用来自螺旋计和耳语镜的五天数据窗口.
使用ROC曲线下的面积 (AUC) 评估模型性能.

主要成果:

ML模型有效地预测了高AUC分数的AECOPD事件.
CatBoost 型号实现了最高的 AUC 0.9721,超过了其他型号.
电子耳语镜的特征 (例如,振动能量) 对于预测比螺旋仪数据更为重要.

结论:

基于树的增强模型对于预测远程监控设置中的AECOPD事件是有效的.
这些模型可以增强家庭COPD患者的早期风险评估和治疗决策.
智能设备数据,特别是来自电子耳机的数据,对于预测COPD恶化非常有价值.

关键词:

慢性阻塞性肺病的急性恶化.在 CatBoost 中使用 CatBoost.机器学习是机器学习.预测模型的预测模型.

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