使用EHR分类数据进行临床决策的自我监督代表性学习的范围审查
Zheng Yuanyuan1,2, Bensahla Adel1,2, Bjelogrlic Mina3,4
1Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
自主监督代表学习 (SSRL) 通过未标记的电子健康记录 (EHR) 数据推动了临床决策. 本综述确定了变压器,自动编码器和GNN模型的趋势,突出了医疗机构的机遇.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 数据科学数据科学数据科学
背景情况:
- 电子健康记录 (EHR) 产生了大量未标记的分类数据.
- 深度学习,特别是自主监督表示学习 (SSRL),提供了一种强大的方法,可以从这些数据中提取有意义的见解.
- 在临床决策过程中,SSRL的整合正在迅速发展.
研究的目的:
- 对使用SSRL用于未标记的分类EHR数据的研究进行范围审查.
- 系统地评估研究趋势,模型家族和SSRL在医疗保健中的应用.
- 确定SSRL在临床实践中的局限性和未来研究机会.
主要方法:
- 在PubMed,MEDLINE,Embase,ACM和Web of Science中进行系统的文献搜索.
- 包括2019年1月至2024年4月期间发表的46项研究.
- 基于PRISMA-ScR (系统性审查的首选报告项目和范围审查的元分析扩展) 准则的分析.
主要成果:
- 基于变压器的模型 (43%) 占主导地位SSRL研究EHR数据,其次是基于Autoencoder (28%) 和基于图形神经网络 (17%) 的模型.
- 鉴定的趋势集中在开发医疗任务的计算和数据高效表示.
- 突出显示了医疗机构采用或开发SSRL技术的实际场景.
结论:
- 通过EHR数据分析,SSRL显示了通过提高临床决策的巨大潜力.
- 需要进一步的研究来克服影响评估的局限性,并充分认识到SSRL对临床实践的影响.
- 医疗机构应该探索利用SSRL来改善数据驱动的洞察力和运营效率.
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