塔科:基于EHR数据的疾病亚型的临床概念和患者访问的任务指导协集群
Ziyang Zhang1, Hejie Cui1, Ran Xu1
1Emory University, Atlanta, Georgia, USA.
概括
TACCO是一个新的框架,通过共同发现患者访问和临床概念子类型来解决电子健康记录 (EHR) 中的复杂疾病异质性. 这有助于改善疾病风险预测和表型分类.
科学领域:
- 生物医学信息学 生物医学信息学
- 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) 为疾病风险预测提供了大量数据.
- 当前的模型往往无法捕捉复杂疾病的异质性和亚型.
- 有需要的方法,模型患者访问和临床概念联合.
研究的目的:
- 引入TACCO,这是一个新的框架,用于共同发现临床概念和患者访问集群.
- 通过使用超图,文本嵌入和自我监督的共同集群来增强EHR数据建模.
- 通过计算疾病亚型来改善疾病风险预测.
主要方法:
- 为EHR数据开发了一个基于超图的框架 (TACCO).
- 实施了由疾病风险预测指导的自我监督的共同集群方法.
- 集成的文本嵌入和对集群对齐的对比目标.
主要成果:
- 与传统的ML基线相比,TACCO实现了平均31.25%的性能改善.
- 与香草超图模型相比,显示了5.26%的改进.
- 通过MIMIC-III和CRADLE数据集的表型分类和心血管风险预测任务进行验证.
结论:
- TACCO通过发现临床概念和患者访问的亚型,有效地模拟疾病异质性.
- 该框架为EHR数据分析提供了改进的实用程序和有洞察力的解释.
- 塔科增强了下游临床任务,如风险预测和表型分类.
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