基于多域图神经网络的疾病预测模型  

Disease Prediction Model Based on Multi-domain Graph Neural Network

作　　者：罗熹[1] 刘洋 安莹[2] LUO Xi;LIU Yang;AN Ying(Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges,Hunan Police Academy,Changsha 410138,China;Big Data Institute,Central South University,Changsha 410083,China)

机构地区：[1]湖南警察学院网络犯罪侦查湖南省普通高校重点实验室,湖南长沙410138 [2]中南大学大数据研究院,湖南长沙410083

出　　处：《湖南大学学报(自然科学版)》2025年第4期124-134,共11页Journal of Hunan University:Natural Sciences

基　　金：湖南省教育厅科学研究重点项目(23A0702)。

摘　　要：电子病历数据类型多样以及时序不规则,现有的基于深度学习的方法在特征学习的过程中大多无法同时兼顾对不同类型临床数据间静态关联和就诊记录间动态时序依赖的有效捕获.针对该问题,本文提出了一种基于多域图神经网络的疾病预测模型.该方法首先利用一个结合编码级注意力和时间感知LSTM的时序特征学习模块获得患者每次就诊的初始特征表示.然后,根据就诊序列中不同就诊间的相关性和时间间隔信息分别构建了一个就诊亲和图和一个就诊时序图,并通过图卷积神经网络从图中挖掘就诊记录间的静态语义关联和动态时序依赖.最后,利用一个基于自注意力机制的多域特征融合模块将时序特征和语义关联特征结合起来得到最终的患者融合特征表示,用于患者未来的疾病预测.在两个真实临床数据集上的实验结果表明,本文方法超过其他现有的方法获得了更高的预测准确性.Due to the characteristics of electronic medical records(EMRs),such as the diversity of data types and temporal irregularity inherent,most existing deep learning-based methods cannot simultaneously capture static correlations between different types of clinical data and dynamic temporal dependencies between visits during the feature learning process.To address this issue,this paper proposes a disease prediction model based on multi-domain graph neural network.In this model,a temporal feature learning module that combines code level attention and time aware LSTM is first utilized to obtain the initial feature representation of patient visits.Then,based on the correlation and time interval information between different visits,a visit affinity graph and a visit sequence graph are constructed,and a graph convolutional neural network is used to mine the static and dynamic semantic associations between visit records from these graphs.Finally,a multi-domain feature fusion module based on self-attention mechanism is utilized to combine temporal features and semantic association features to obtain the final patient fusion representation for future disease prediction.The experimental results on two real clinical datasets show that our method outperforms other existing methods and achieves higher prediction accuracy.

关 键 词：电子病历 疾病预测 图神经网络 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]