GatorCLR:使用自我监督的对比图表表示方式,对电子健康记录中的患者结果进行个性化预测
Yuxi Liu1, Zhenhao Zhang2, Jiacong Mi3
1Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, 32610, FL, USA.
Journal of biomedical informatics
|June 4, 2025
概括
我们开发了GatorCLR,一种自我监督的学习模型,用于分析电子健康记录 (EHR) 以更好地预测患者的结果. 这种方法有效地捕获纵向数据模式,以改善临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 电子健康记录 (EHR) 数据分析对于预测患者结果至关重要.
- 纵向EHR数据包含有价值的模式,用于预测未来的健康状况.
- 目前的方法需要有效的方式来学习复杂的纵向EHR数据.
研究的目的:
- 调查和设计自主监督学习 (SSL) 范式,以提供高质量的EHR数据表示.
- 通过捕捉纵向关系和模式来改善患者结果预测.
- 开发一个强大的模型来分析纵向EHR数据.
主要方法:
- 提出了GatorCLR,一个端到端对比的SSL模型.
- 集成图形分析用于患者建模,创建患者关系的图形表示.
- 利用双层增强技术进行一致的,保持身份的图形增强.
主要成果:
- 在现实世界EHR数据集上,GatorCLR表现出了有意义和强大的性能.
- 该模型在多个临床任务中显示出有效性.
- 该方法在模型决策中提供了透明度.
结论:
- GatorCLR在开发用于纵向EHR数据的基础模型方面取得了重大进展.
- 该模型可适应各种下游预测分析任务.
- 这项研究为使用EHR数据进行预测分析的从业人员提供了价值.
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