基于机器学习的喘发作预测模型从例行收集的电子健康记录:系统范围审查
Arif Budiarto1,2, Kevin C H Tsang1, Andrew M Wilson3,4
1Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.
JMIR AI
|June 14, 2024
概括
使用电子健康记录的机器学习模型显示出预测喘发作的前景. 然而,该领域需要更强大的验证和标准化报告用于临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 电子健康记录 (EHR) 为开发预测工具提供了有价值的患者历史数据.
- 机器学习 (ML) 与电子健康记录相结合,为创建喘发作早期预警系统提供了机会.
- 现有的研究已经探索了基于ML的工具来预测喘发作.
研究的目的:
- 批判性地评估使用EHR开发的机器学习 (ML) 模型,用于预测喘发作.
- 评估ML模型在喘发作预测中的方法,性能和报告.
主要方法:
- 在PubMed和Scopus (2012-2023) 的系统文献搜索中,寻找使用EHR来预测喘发作的研究.
- 纳入标准侧重于EHR数据,喘发作结果和ML模型的比较性能.
- 不包括非英语,非研究和缺乏方法细节的论文;概括了关于数据预处理,算法,验证,可解释性和实现的研究.
主要成果:
- 17项研究符合纳入标准,揭示了喘发作定义的显著异质性.
- 极端的阶级不平衡是常见的 (76%),只有38%的人解决了它.
- 梯度增强是表现最好的ML方法 (59%); 82%使用模型解释,但没有研究遵循报告准则或进行前性验证.
结论:
- 基于ML的喘发作预测领域使用EHR是不发达的.
- 关键的挑战包括数据异质性,类不平衡,缺乏外部验证和低于最佳的报告.
- 解决这些技术挑战对于推动这些预测工具的临床采用至关重要.
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