计算机与数字工程2025，Vol.53Issue(3)：684-691,8.DOI:10.3969/j.issn.1672-9722.2025.03.013
基于加权贝叶斯网络的心脏破裂风险预测模型
Risk Prediction Model of Cardiac Rupture Based on Weighted Bayesian Network
摘要
Abstract
As the most fatal complication of acute myocardial infarction,the prediction and intervention of cardiac rupture af-ter myocardial infarction is particularly important.Because heart rupture is a rare disease with high mortality,its data set there is im-balance and data deletion,which makes it difficult for the deep learning model to achieve high accuracy,and the results of the mod-el need to be interpretable.In order to solve the above problems,this paper proposes a weighted Bayesian network model based on attention mechanism.The model builds a more accurate network structure by combining medical knowledge and algorithms.Second-ly,by integrating attention weight into Bayesian network,more attention can be paid to significance indicators,and the accuracy and interpretability of the model are enhanced.Finally,on the real data,the risk of cardiac rupture after acute myocardial infarction is evaluated.The experimental results show that the accuracy and interpretability of the model are better,and its F1 score and AUC value can reach 0.771 8 and 0.798 7 respectively.关键词
疾病分类/电子健康记录/注意力机制/贝叶斯网络/心脏破裂Key words
disease prediction/electronic health record/attention mechanism/Bayesian network/cardiac rupture分类
数学引用本文复制引用
刘初阳,杨湘,陈艳红..基于加权贝叶斯网络的心脏破裂风险预测模型[J].计算机与数字工程,2025,53(3):684-691,8.基金项目
国家自然科学基金项目(编号:U1836118)资助. （编号:U1836118）
