Jinghua Cui, Chendi Zhu, M. Zhu, Qingfeng Yin, Ya Hou
{"title":"基于随访数据的冠心病知识组织研究","authors":"Jinghua Cui, Chendi Zhu, M. Zhu, Qingfeng Yin, Ya Hou","doi":"10.5771/0943-7444-2023-1-11","DOIUrl":null,"url":null,"abstract":"Coronary heart disease was the main reason behind the millions of deaths caused by heart attacks in patients over the last decades. This study is a knowledge organization study of coronary heart disease based on follow-up data. Firstly, we refer to some medical ontologies on the Bioportal webpage and extract some entities and define them based on the BFO top-level ontology, then summarize their attributes and construct semantic model to form semantic relationships, and finally use Protégé to form a coronary heart disease ontology based on follow-up data, and store and visualize it with the help of GraphDB. The visualization graph finally formed in this study enables the data of each follow-up visit to be reflected on a visualization interface in a centralized and systematic way at the same time, thus helping physicians to browse patient information comprehensively, intuitively and quickly in order to find the key factors affecting the treatment outcome.","PeriodicalId":46091,"journal":{"name":"Knowledge Organization","volume":"1 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Coronary Heart Disease Knowledge Organization Based on Follow-up Data\",\"authors\":\"Jinghua Cui, Chendi Zhu, M. Zhu, Qingfeng Yin, Ya Hou\",\"doi\":\"10.5771/0943-7444-2023-1-11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Coronary heart disease was the main reason behind the millions of deaths caused by heart attacks in patients over the last decades. This study is a knowledge organization study of coronary heart disease based on follow-up data. Firstly, we refer to some medical ontologies on the Bioportal webpage and extract some entities and define them based on the BFO top-level ontology, then summarize their attributes and construct semantic model to form semantic relationships, and finally use Protégé to form a coronary heart disease ontology based on follow-up data, and store and visualize it with the help of GraphDB. The visualization graph finally formed in this study enables the data of each follow-up visit to be reflected on a visualization interface in a centralized and systematic way at the same time, thus helping physicians to browse patient information comprehensively, intuitively and quickly in order to find the key factors affecting the treatment outcome.\",\"PeriodicalId\":46091,\"journal\":{\"name\":\"Knowledge Organization\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge Organization\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.5771/0943-7444-2023-1-11\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge Organization","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.5771/0943-7444-2023-1-11","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
Research on Coronary Heart Disease Knowledge Organization Based on Follow-up Data
Coronary heart disease was the main reason behind the millions of deaths caused by heart attacks in patients over the last decades. This study is a knowledge organization study of coronary heart disease based on follow-up data. Firstly, we refer to some medical ontologies on the Bioportal webpage and extract some entities and define them based on the BFO top-level ontology, then summarize their attributes and construct semantic model to form semantic relationships, and finally use Protégé to form a coronary heart disease ontology based on follow-up data, and store and visualize it with the help of GraphDB. The visualization graph finally formed in this study enables the data of each follow-up visit to be reflected on a visualization interface in a centralized and systematic way at the same time, thus helping physicians to browse patient information comprehensively, intuitively and quickly in order to find the key factors affecting the treatment outcome.