软件导刊2025，Vol.24Issue(9)：48-54,7.DOI:10.11907/rjdk.241496
基于电子病历时间嵌入的编码器稠密网络冠心病预测模型
EMR-Based Time Embedding Encode-Dense Network Model for Coronary Heart Disease Prediction
摘要
Abstract
Early detection of coronary heart disease can significantly improve the cure rate,but the early detection rate of coronary heart dis-ease is low due to the inconspicuous prodromal symptoms and lack of self-awareness of patients.By constructing the E-TEED-CHD early pre-diction model based on the EMR-based time embedding encode-dense network,it is possible to effectively use the non-structured electronic medical record data from residents across regions,institutions,and diseases for early prediction of coronary heart disease.The E-TEED-CHD model embeds the residents' electronic medical records with time position information and parallelly inputs them into multiple encoders for ac-celerated processing.A dense network model is used to parse the outputs of the encoders and achieve early prediction of coronary heart disease.Furthermore,the E-TEED-CHD model uses a novel medical record masking technique to ignore the quantity differences of electronic medical records among patients,achieving unified processing of different numbers of EMRs with a streamlined structure.Ablation studies demonstrate the effectiveness of the time embedding method and the encode-dense network model.Evaluation experiments show that the early coronary heart disease prediction accuracy of the E-TEED-CHD model,after data preprocessing and hyperparameter tuning,is 98.71%.And the aver-age training time for electronic medical records of 1,000 residents is 17.752 seconds.Both accuracy and training speed of E-TEED-CHD mod-el outperform the other state-of-the-art models.关键词
电子病历/时间嵌入/稠密网络/编码器/冠心病预测Key words
electronic medical record/time embedding/dense network/encoder/coronary heart disease prediction分类
信息技术与安全科学引用本文复制引用
陈艳章,韩文静,刘增光..基于电子病历时间嵌入的编码器稠密网络冠心病预测模型[J].软件导刊,2025,24(9):48-54,7.基金项目
潍坊市科学技术发展计划项目(2023GX063) （2023GX063）
