Intelligent generation of personalized cardiovascular healthcare program based on knowledge network

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications Pub Date : 2025-06-25 DOI:10.1016/j.eswa.2025.128718

Yaqin Lin , Weiqiang Zhang , Quanchen Liu

{"title":"Intelligent generation of personalized cardiovascular healthcare program based on knowledge network","authors":"Yaqin Lin , Weiqiang Zhang , Quanchen Liu","doi":"10.1016/j.eswa.2025.128718","DOIUrl":null,"url":null,"abstract":"<div><div>We convert a knowledge base for Cardiovascular disease into a fuzzy Petri net and use the parallel inference method based on matrix operations to intelligently generate the healthcare programs involving exercise, diet, living, and auxiliary medication plans. The algorithms used by our system consider personal health characteristics and environmental factors to extract valuable health information. By analyzing this information, the system can provide users with accurate recommendations and personalized health insights, significantly enhancing their overall well-being. Besides, the method can automatically identify and resolve contradictory rules in knowledge inference, ensuring the validity and accuracy of program recommendations. Our recommendation algorithms create personalized diet plans based on income, preferences, and restrictions. We also classify and suggest diet plans based on the nine diet categories in the Chinese Food Guide Pagoda. We present a case study of Cardiovascular disease to showcase the effectiveness and efficiency of our method.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"294 ","pages":"Article 128718"},"PeriodicalIF":7.5000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095741742502336X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

We convert a knowledge base for Cardiovascular disease into a fuzzy Petri net and use the parallel inference method based on matrix operations to intelligently generate the healthcare programs involving exercise, diet, living, and auxiliary medication plans. The algorithms used by our system consider personal health characteristics and environmental factors to extract valuable health information. By analyzing this information, the system can provide users with accurate recommendations and personalized health insights, significantly enhancing their overall well-being. Besides, the method can automatically identify and resolve contradictory rules in knowledge inference, ensuring the validity and accuracy of program recommendations. Our recommendation algorithms create personalized diet plans based on income, preferences, and restrictions. We also classify and suggest diet plans based on the nine diet categories in the Chinese Food Guide Pagoda. We present a case study of Cardiovascular disease to showcase the effectiveness and efficiency of our method.

查看原文本刊更多论文

基于知识网络的个性化心血管医疗方案智能生成

将心血管疾病知识库转化为模糊Petri网，采用基于矩阵运算的并行推理方法，智能生成运动、饮食、生活、辅助用药等健康计划。本系统采用的算法考虑了个人健康特征和环境因素，以提取有价值的健康信息。通过分析这些信息，系统可以为用户提供准确的建议和个性化的健康见解，显著提高他们的整体健康水平。此外，该方法还可以自动识别和解决知识推理中的矛盾规则，保证程序推荐的有效性和准确性。我们的推荐算法根据收入、偏好和限制创建个性化的饮食计划。我们还根据《中国饮食指南宝塔》中的九大饮食类别对饮食计划进行了分类和建议。我们提出一个心血管疾病的案例研究，以展示我们的方法的有效性和效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Expert Systems with Applications 工程技术-工程：电子与电气

CiteScore

13.80

自引率

10.60%

发文量

2045

审稿时长

8.7 months

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.