DeepJ:用于患者轨迹建模的可差聚合的图形卷积变压器
Deyi Li1, Zijun Yao2, Muxuan Liang3
1Department of Health outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
深度患者旅程 (DeepJ) 模型跨患者遭遇的医疗事件相互作用,改善患者结果预测. 这种图形学习方法捕捉了时间依赖性,以便更好地分层风险.
科学领域:
- 医疗信息学医学信息学
- 医疗保健中的人工智能
- 电子健康记录的图形学习
背景情况:
- 电子健康记录 (EHR) 数据包含复杂的医疗事件相互作用.
- 现有的图形学习方法与纵向数据扎,无法建模交叉遇到的时间依赖.
- 静态图方法限制了对患者随时间的旅程的分析.
研究的目的:
- 为了介绍Deep Patient Journey (DeepJ),一个新的图形卷积变压器模型.
- 为了有效地捕捉内部遭遇和相互遭遇的医疗事件相互作用.
- 为了确定时间和功能相关的医疗事件的群体,以预测患者的结果.
主要方法:
- 开发了DeepJ,一个图形卷积变压器模型.
- 集成的可微分图集群用于增强的交互建模.
- 应用DeepJ对结构化EHR数据进行纵向分析.
主要成果:
- DeepJ成功地捕获了医疗事件的互动.
- 确定了与患者结果相关的关键事件集群.
- 在预测任务中表现优于五个最先进的基线模型.
结论:
- DeepJ 提供了使用 EHR 数据改进的患者旅程建模.
- 该模型增强了可解释性,并证明了患者风险分层的潜力.
- DeepJ推进了图形学习在临床信息学中的应用.
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