{"title":"COVID-19医疗数据集成方法","authors":"V. Todorova, Veska Gancheva, V. Mladenov","doi":"10.37394/232023.2022.2.11","DOIUrl":null,"url":null,"abstract":"The need to create automated methods for extracting knowledge from data arises from the accumulation of a large amount of data. This paper presents a conceptual model for integrating and processing medical data in three layers, comprising a total of six phases: a model for integrating, filtering, sorting and aggregating Covid-19 data. A medical data integration workflow was designed, including steps of data integration, filtering and sorting. The workflow for Covid-19 medical data from clinical records of 20400 potential patients was employed.","PeriodicalId":336407,"journal":{"name":"MOLECULAR SCIENCES AND APPLICATIONS","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"COVID-19 Medical Data Integration Approach\",\"authors\":\"V. Todorova, Veska Gancheva, V. Mladenov\",\"doi\":\"10.37394/232023.2022.2.11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The need to create automated methods for extracting knowledge from data arises from the accumulation of a large amount of data. This paper presents a conceptual model for integrating and processing medical data in three layers, comprising a total of six phases: a model for integrating, filtering, sorting and aggregating Covid-19 data. A medical data integration workflow was designed, including steps of data integration, filtering and sorting. The workflow for Covid-19 medical data from clinical records of 20400 potential patients was employed.\",\"PeriodicalId\":336407,\"journal\":{\"name\":\"MOLECULAR SCIENCES AND APPLICATIONS\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MOLECULAR SCIENCES AND APPLICATIONS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37394/232023.2022.2.11\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MOLECULAR SCIENCES AND APPLICATIONS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37394/232023.2022.2.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The need to create automated methods for extracting knowledge from data arises from the accumulation of a large amount of data. This paper presents a conceptual model for integrating and processing medical data in three layers, comprising a total of six phases: a model for integrating, filtering, sorting and aggregating Covid-19 data. A medical data integration workflow was designed, including steps of data integration, filtering and sorting. The workflow for Covid-19 medical data from clinical records of 20400 potential patients was employed.
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