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基于机器学习的早期预测使用电子健康记录的败血症:系统性审查

Khandaker Reajul Islam¹, Johayra Prithula², Jaya Kumar¹

¹Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia.

Journal of clinical medicine

|September 9, 2023

概括

机器学习和深度学习显示出使用电子健康记录预测败血症的前景. 这一系统性审查强调了它们在临床实践中及时检测和干预的重要性.

科学领域:

医疗信息学医疗信息学
计算生物学计算生物学
临床数据科学临床数据科学

背景情况:

败血症是一种危急,危及生命的疾病,需要及时诊断.
早期检测败血症对于改善患者的结果和预防严重进展至关重要.
机器学习 (ML) 和深度学习 (DL) 提供了使用电子健康记录 (EHR) 进行败血症预测的潜力.

研究的目的:

系统地审查ML / DL在预测使用EHRs的败血症发作中的应用.
评估该领域现有研究的方法和发现.
评估关于ML/DL用于败血症预测的研究质量和一致性.

主要方法:

在PubMed,IEEE Xplore,Google Scholar和Scopus中进行了系统的文献搜索.
包括使用ML/DL用于成人败血症检测或EHRs早期预测的研究.
对选定的研究进行了数据提取,分析和质量评估.

主要成果:

从1942篇文章中选择了42项研究,主要是回顾性和基于美国的.
记录了各种数据集,败血症定义和模型参数,需要数据增强.
纵向EHR数据促进了早期败血症预测,尽管研究质量和资金相关性各不相同.

结论:

ML/DL方法对于败血症检测和早期预测具有重要意义.
结合ML/DL的EHR数据,对临床败血症管理具有相当大的潜力.
需要进一步的标准化和质量评估,以优化ML/DL在败血症中的应用.

关键词:

深度学习是一种深度学习.早期预测早期预测电子健康记录电子健康记录紧急情况部门 (ED)重症监护室 (ICU) 的重症监护室机器学习是机器学习.这是一种血症.

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