一个多式堆叠组合模型用于预测心脏输出,利用在全身麻醉期间的心肺呼吸相互作用
1Anaesthesiology and Intensive Care Medicine, Medius CLINIC NÜRTINGEN-Academic Teaching Hospital of the University of Tübingen, Auf dem Säer 1, 72622, Nürtingen, Germany. albiondervishi@gmail.com.
Scientific reports
|March 30, 2024
概括
这项研究开发了一种机器学习模型,用于在全身麻醉期间使用心肺数据估计心脏输出 (CO). 该模型与现有的二氧化碳监测设备有很好的一致性.
科学领域:
- 麻醉学 麻醉学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 准确的心脏输出 (CO) 监测在全身麻醉期间对于患者管理至关重要.
- 现有的CO监测方法,如脉冲轮技术,都有局限性.
- 整合心肺相互作用可能会改善CO估计.
研究的目的:
- 开发和验证用于估计心脏输出 (CO) 的多式堆叠机器学习模型.
- 评估模型的性能与已有的CO监测设备 (Vigileo和EV1000) 相比.
主要方法:
- 使用了469名接受全身麻醉的成年患者的回顾性数据集.
- 采用多式联动堆叠组合,随机森林,GLM,GBM和XGBoost作为基础学习者,GLM作为metalearner.
- 分析了血液动力学 (血压,心率,SpO2) 和呼吸 (通风参数,潮尾CO2) 数据.
主要成果:
- 多式联接堆叠组合模型与EV1000 (偏差 -0.001 L/min, -0.271%) 和Vigileo (偏差 -0.01 L/min, -0.333%) 的CO监测器表现出良好的一致性.
- 布兰德-阿尔特曼分析证实了该模型对心脏输出的预测准确性.
结论:
- 一个多式堆叠机器学习模型可以准确地估计在全身麻醉期间的心脏输出.
- 这种方法为CO监测提供了一种有希望的非侵入性方法,可能提高患者的安全性.
相关概念视频
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