预测建模与时间图形表示在电子健康记录上的预测建模.
Jiayuan Chen1, Changchang Yin1, Yuanlong Wang1
1The Ohio State University.
IJCAI : proceedings of the conference
|October 3, 2024
概括
这项研究引入了一种新的时间异质图和时间图转换器 (TRANS),以有效地表示患者电子健康记录 (EHR). TRANS捕获了时间和结构的EHR信息,实现了最先进的预测性能.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于医疗信息学
- 深度学习用于预测建模.
背景情况:
- 电子健康记录 (EHR) 对于基于深度学习的医疗预测至关重要.
- 现有的方法很难有效地整合时间和结构EHR信息.
- 序列模型捕获时间,但错过了结构数据;图形模型捕获结构,但错过了时间动态.
研究的目的:
- 开发一种新型患者电子病历表征,将时间和结构信息整合在一起.
- 引入一个时间图形转换器 (TRANS) 进行增强的EHR分析.
- 提高医疗保健中基于深度学习的预测模型的准确性.
主要方法:
- 模拟患者的EHR作为一个时间异质图,带有访问和医疗事件节点.
- 开发了TRANS,结合了时间边缘特征,位置编码和图形卷积.
- 综合结构化信息传播和健康状况变化的时间感知节点.
主要成果:
- 拟议的时间异质图有效地捕捉了时间和结构EHR数据.
- 在整合多样化的EHR信息方面,TRANS表现出卓越的表现.
- 在三个真实世界数据集上进行了广泛的实验,证实了最先进的结果.
结论:
- 新的时间异质图和TRANS模型在EHR表示中提供了显著的进步.
- 这种方法增强了医疗预测的深度学习模型能力.
- TRANS提供了一个强大的框架,可以利用复杂的EHR数据.
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