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利用机器学习来预测机械通风的成功断奶:一个系统的审查

Fatma Refaat Ahmed¹, Nabeel Al-Yateem², Seyed Aria Nejadghaderi³

¹Department of Nursing, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates; Critical Care and Emergency Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt.

Australian critical care : official journal of the Confederation of Australian Critical Care Nurses

|March 9, 2025

概括

机器学习模型准确地预测了成年人机械通风的成功断奶. 极端梯度提升 (XGBoost) 在本系统性审查中评估的各种算法中表现出卓越的性能.

科学领域:

关键护理医学关键护理医学
生物医学信息学生物医学信息学
计算生物学计算生物学

背景情况:

机器学习 (ML) 模型越来越多地用于预测成人患者机械通风 (MV) 断奶的结果.
ML对MV断奶的应用是一个相对较新的但快速发展的领域.
本系统性审查评估了ML在预测MV成功断奶方面的有效性.

研究的目的:

系统地审查和评估机器学习模型在预测机械通风成功断奶方面的表现.
为了确定最有效的ML算法和方法来预测MV断奶.
突出呼吸系统护理ML应用中的差距和未来方向.

主要方法:

在PubMed,EMBASE,Scopus,Web of Science,Google Scholar,ACM数字图书馆和IEEE Xplore中进行全面的文献搜索,截至2024年5月.
包括专家评审的研究,重点是ML模型,用于预测成年患者成功的MV断奶.
使用Joanna Briggs研究所修改后的检查清单进行包含研究的质量评估.

主要成果:

包括11项涉及18336名患者的研究.
像XGBoost和Light Gradient-Boosting Machine这样的增强算法是最常见的,其次是随机森林和神经网络.
XGBoost在曲线下面面积的比较中总体上表现优越,高质量的研究占主导地位.

结论:

机器学习模型是预测成年患者机械通风成功断奶的有效工具.
XGBoost成为了这个预测任务的高性能算法.
未来的研究应该探索先进的架构,如变压器模型,以进一步提高预测准确性.

关键词:

在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.人工神经网络的人工神经网络机器学习是机器学习.机械通风机械通风机械通风机系统性审查系统性审查断奶断奶断奶断奶断奶在XGBoost中使用.

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