论文标题
在线疾病自我诊断与感应异质图卷积网络
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
论文作者
论文摘要
我们提出了一个医疗保健图卷积网络（HEALGCN），以基于电子医疗记录（EHRS）为在线用户提供疾病自我诊断服务。在本文中，针对在线疾病诊断的两个主要挑战：（1）通过图形卷积网络为冷启动使用者提供服务，（2）通过症状检索系统处理稀缺的临床描述。为此，我们首先将EHR数据组织成一个异质图，该图能够对用户，症状和疾病之间的复杂相互作用进行建模，并根据电感学习范式调整图表的学习对疾病诊断。然后，我们建立一个疾病自我诊断系统，该系统具有相应的基于EHR的症状检索系统（GraphRet），该系统可以通过追踪预定义的元路径来搜索并提供相关替代症状的列表。当面对用户稀缺描述时，GraphRet有助于丰富通过EHR图设定的种子症状，因此可以提高诊断精度。最后，我们在大规模的EHR数据集上验证了模型的优势。
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main challenges are focused in this paper for online disease diagnosis: (1) serving cold-start users via graph convolutional networks and (2) handling scarce clinical description via a symptom retrieval system. To this end, we first organize the EHR data into a heterogeneous graph that is capable of modeling complex interactions among users, symptoms and diseases, and tailor the graph representation learning towards disease diagnosis with an inductive learning paradigm. Then, we build a disease self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval System (GraphRet) that can search and provide a list of relevant alternative symptoms by tracing the predefined meta-paths. GraphRet helps enrich the seed symptom set through the EHR graph when confronting users with scarce descriptions, hence yield better diagnosis accuracy. At last, we validate the superiority of our model on a large-scale EHR dataset.
