在接受机械通风的儿童中,使用机器学习对动脉二氧化碳部分压力的个性化估计
Hye-Ji Han1, Bongjin Lee2,3, June Dong Park1
1Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children's Hospital, Seoul, 03080, Republic of Korea.
BMC pediatrics
|February 29, 2024
概括
这项研究开发了机器学习模型,以在机械通风的儿科患者中非侵入性地估计二氧化碳 (PaCO2) 的动脉部分压力,为侵入性监测提供了有希望的替代方案.
科学领域:
- 儿科重症监护医药 儿科重症监护医药
- 生物医学工程 生物医学工程
- 机器学习在医疗保健中的应用.
背景情况:
- 精确测量二氧化碳 (PaCO2) 的动脉部分压力对于机械通风至关重要.
- 目前的侵入性方法存在风险;由于通风-透气不匹配,非侵入性替代品,如终潮二氧化碳 (EtCO2) 的准确性有限.
研究的目的:
- 开发和评估机器学习模型,用于在需要机械通风的儿科患者中进行PaCO2的非侵入性估计.
- 为了比较不同机器学习算法的性能,预测PaCO2.
主要方法:
- 对儿童患者 (<18岁) 进行机械通风的回顾性观察研究.
- 利用临床数据和实验室结果用于训练和测试线性回归,多层感知子和极端梯度增强模型.
- 使用R2值评估模型性能,与7:3列车测试分割.
主要成果:
- 分析了来自32名儿科患者的2,427个测量结果.
- 极端梯度增强模型实现了0.877的最高R2,超过了线性回归 (0.799) 和多层感知子 (0.851).
- 开发的模型显示,与非侵入性EtCO2测量相比,与PaCO2的相关性更高.
结论:
- 机器学习模型可以有效地估计PaCO2在机械通风的儿科患者中进行非侵入性评估.
- 开发的模型表现出可接受的性能,比传统方法提供了潜在的改进.
- 为了提高这些模型的可靠性和外部验证,需要进一步的研究.
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