湖南大学学报（自然科学版）2025，Vol.52Issue(4)：124-134,11.DOI:10.16339/j.cnki.hdxbzkb.2025272
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
摘要
Abstract
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Key words
electronic medical records/disease prediction/graph neural network/attention mechanism分类
信息技术与安全科学引用本文复制引用
罗熹,刘洋,安莹..基于多域图神经网络的疾病预测模型[J].湖南大学学报（自然科学版）,2025,52(4):124-134,11.基金项目
湖南省教育厅科学研究重点项目(23A0702),Key Scientific Research Project of Hunan Provincial Department of Education(23A0702) （23A0702）
