基于电子健康记录进行临床风险预测的图形神经网络:一项调查调查
Heloísa Oss Boll1, Ali Amirahmadi2, Mirfarid Musavian Ghazani2
1Institute of Informatics, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves, 9500, Porto Alegre, 91501-970, RS, Brazil; School of Information Technology, Halmstad University, Kristian IV:s väg 3, Halmstad, 301 18, Sweden.
Journal of biomedical informatics
|February 29, 2024
概括
图形神经网络 (GNN) 显示出使用电子健康记录 (EHR) 进行临床风险预测的前景. 未来的研究应该解决数据挑战,通过更准确的GNN模型来改善患者护理.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于临床应用.
背景情况:
- 电子健康记录 (EHR) 包含复杂的关系数据,对临床风险预测有价值.
- 图形神经网络 (GNN) 提供了一个强大的方法来建模这些复杂的关系.
- 现有研究突出显示,在这个领域,GNN的兴趣和应用日益增长.
研究的目的:
- 通过使用EHR数据进行临床风险预测中GNN应用的全面审查.
- 为提供基于GNN的临床风险预测当前最先进的现状的概述.
- 确定关键的研究趋势,流行的方法和现场挑战.
主要方法:
- 在主要数据库 (Scopus,PubMed,ACM,Embase) 进行系统的文献搜索.
- 包括2009年1月至2023年5月期间发表的英语原创研究论文.
- 从50篇选定的文章中提取和分析数据.
主要成果:
- 自2020年以来,观察到临床风险预测的GNN出版物大幅增加.
- 诊断预测是最常见的应用 (36篇文章).
- 图形注意网络 (GAT) 是最常用的GNN架构 (19篇文章),MIMIC-III是主要的数据源 (23篇文章).
结论:
- 由于它们能够处理关系数据和大量的EHR数据,GNN是临床风险预测的有效工具.
- 未来的工作应该集中在克服诸如EHR数据异质性,多模式性和模型可解释性等挑战上.
- 开发整体的GNN模型对于提高预测准确性和临床实施至关重要,以改善患者的治疗结果.
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