对用于ARDS管理,预测和分类的机器学习模型进行系统审查
Tu K Tran1,2, Minh C Tran3, Arun Joseph3
1Department of Engineering and Science, University of Oxford, Oxford, UK. tu.tran@wolfson.ox.ac.uk.
Respiratory research
|June 4, 2024
概括
机器学习 (ML) 在治疗急性呼吸困扰综合征 (ARDS) 中表现有前途. 极端梯度提升对于较小的数据集是有效的,而神经网络需要更大的数据集来改善ARDS预测和分类.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 呼吸系统医学 呼吸系统医学
背景情况:
- 急性呼吸困扰综合征 (ARDS) 是一种严重的呼吸衰竭.
- 机器学习 (ML) 和人工智能 (AI) 为ARDS管理提供先进的解决方案.
- 这些技术有助于分类,事件检测和预测建模.
研究的目的:
- 系统地审查在ARDS管理中应用ML和AI的研究.
- 描述ML中用于ARDS预测和分类的最新技术.
- 为ARDS确定可解释AI的未来研究方向.
主要方法:
- 在Google Scholar,PubMed和EBSCO (2009-2023) 上进行了系统的文献搜索.
- 选了243项研究,包括52项用于审查和分析.
- 审查整合了ML模型和可解释性的现有知识.
主要成果:
- 渐变增强是最常见的方法 (23.1%的研究).
- 由于数据限制,神经网络在15.4%的研究中被使用.
- 可解释性方法,主要是特征重要性,在28.8%的研究中存在.
结论:
- 极端梯度增强对于5000个样本以下的数据集非常成功.
- 大规模的多中心数据集对于减少偏差和利用神经网络至关重要.
- 一个用于验证和向临床医生解释ML模型的框架对于ARDS管理至关重要.
相关概念视频
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The underlying physiological abnormalities that contribute to hypoxemic respiratory failure include:
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