机器学习方法用于从患者数据中发现临床途径:系统审查
Lillian Muyama1, Antoine Neuraz2, Adrien Coulet1
1Inria Paris, Paris, 75013, France; Centre de Recherche des Cordeliers, Inserm, Université Paris Cité, Sorbonne Université, Paris, 75006, France.
机器学习方法可以从患者数据中发现临床途径,帮助医疗保健决策. 集群和过程挖矿等无监督技术占主导地位,但专家的验证对于可重现性至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 医疗保健中的人工智能
背景情况:
- 临床途径是患者护理事件的结构化序列,对于医疗保健评估和决策支持至关重要.
- 机器学习 (ML) 提供了强大的方法,可以从复杂的医疗数据中发现这些途径.
- 本综述综合了从患者记录中发现临床途径的ML应用.
研究的目的:
- 提供关于临床途径发现ML方法的综合文献概述.
- 在数据驱动路径分析中识别趋势,常见的技术和动机.
- 突出ML衍生途径在改善临床决策方面的潜力.
主要方法:
- 按照PRISMA指南进行了一次系统的文献审查.
- 从2004年到2023年,在六个主要的科学数据库 (ACM,ScienceDirect,Web of Science,PubMed,IEEE Xplore,Scopus) 中进行了搜索.
- 确定了131篇相关论文,并对其方法和目标进行了分析.
主要成果:
- 鉴定的研究中有很大一部分 (67.2%) 使用了无监督的ML技术.
- 聚类和过程挖掘是最常用的无监督方法,用于发现路径.
- 发现的动机从知识提取到确保遵守临床指南.
结论:
- 机器学习有效地从患者数据中发现临床途径,为增强的临床决策支持提供了潜力.
- ML方法的可复制性对于可靠的路径发现至关重要.
- 临床专家对发现的途径进行严格的验证是必要的,以确保其临床实用性和准确性.
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