Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs
Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs
复制标题
DOI：
10.1609/aaai.v36i4.20380
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Chang Lu;Tian Han;Yue Ning
中科院分区：
文献类型：
--
作者：
Chang Lu;Tian Han;Yue Ning
随着电子病历（EHR）在医疗机构的广泛应用，基于深度学习的健康事件预测越来越受到人们的关注。用于基于深度学习的预测的EHR数据的一个共同特征是历史诊断。现有的工作主要是将诊断视为一种独立的疾病，而没有考虑就诊中疾病之间的临床关系。许多机器学习方法假设疾病表征在患者的不同就诊中是静态的。但在实际操作中，多种疾病经常同时被诊断出来，反映出有利于预后的隐性规律。此外，疾病的发展不是一成不变的，因为有些疾病可以在病人的不同就诊中出现或消失，并表现出不同的症状。为了有效地利用这种组合疾病信息并探索疾病的动态，我们提出了一种新的上下文感知学习框架，该框架使用动态疾病图的转换函数。具体来说，我们为疾病组合构造了一个具有多个节点属性的全局疾病共现图。我们为每位患者的访问设计了动态子图，以利用全局和局部上下文。我们进一步定义了三个诊断角色在每次访问基于节点属性的变化，以模拟疾病的过渡过程。在两个现实世界的电子病历数据集上的实验结果表明，所提出的模型在预测健康事件方面优于目前的技术水平。
With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease and does not consider clinical relations among diseases in a visit. Many machine learning approaches assume disease representations are static in different visits of a patient. However, in real practice, multiple diseases that are frequently diagnosed at the same time reflect hidden patterns that are conducive to prognosis. Moreover, the development of a disease is not static since some diseases can emerge or disappear and show various symptoms in different visits of a patient. To effectively utilize this combinational disease information and explore the dynamics of diseases, we propose a novel context-aware learning framework using transition functions on dynamic disease graphs. Specifically, we construct a global disease co-occurrence graph with multiple node properties for disease combinations. We design dynamic subgraphs for each patient's visit to leverage global and local contexts. We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes. Experimental results on two real-world EHR datasets show that the proposed model outperforms state of the art in predicting health events.