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使用机器学习预测缺氧:系统审查
Lena Pigat1, Benjamin P Geisler1, Seyedmostafa Sheikhalishahi1
1Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
JMIR medical informatics
|February 8, 2024
概括
机器学习模型显示出预测住院患者缺氧的前景. 深度学习和仅使用外围氧和度的模型,特别是长期短期记忆算法,表现出强大的预测性能.
科学领域:
- 医疗信息学 医疗信息学
- 临床预测模型临床预测模型
- 医疗保健中的人工智能
背景情况:
- 缺氧是一种关键的危险因素,也是住院患者健康状况下降的指标.
- 预测缺氧事件对于及时干预和预防患者病情恶化至关重要.
研究的目的:
- 系统地审查和比较用于预测住院患者缺氧事件的机器学习模型.
- 分析现有研究的方法,预测性能和患者群体.
主要方法:
- 在主要数据库 (Web of Science,Embase,MEDLINE,Google Scholar) 进行系统的文献搜索.
- 包括使用机器学习在住院患者中预测缺氧/缺氧的研究.
- 使用预测模型偏差风险评估工具进行偏差风险评估.
主要成果:
- 分析了12篇论文和32个模型,揭示了各种方法和种群.
- 大多数研究 (83%) 存在不清楚或高偏差风险,限制了可比性.
- 总体预测性能中等至高;深度学习模型的性能与传统的ML相比相当或更好.
- 仅使用外周氧和的模型,通常具有长期短期记忆 (LSTM),表现出卓越的性能.
结论:
- 机器学习模型可以使用回顾性数据准确预测缺氧事件.
- 研究异质性和偏见需要进一步的验证研究,以实现概括性和可靠的预测性绩效评估.
关键词:
无氧 没有氧气 没有氧气人工智能的人工智能是人工智能.恶化的恶化.医院 医院 医院 医院低氧症的低氧症是什么缺氧 缺氧是指缺氧的情况.低氧气的情况是这样的.机器学习是机器学习.氧气 氧气 氧气 氧气预测 预测 预测 预测预测 预测 预测 预测预测性 预测性 预测性审查方法论 审查方法论审查方法 审查方法.这是一个系统的系统的系统的系统.系统性审查 系统性审查更多相关视频
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