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전자의무기록 데이터 분석 접근법

DC Field Value Language
dc.contributor.author윤덕용-
dc.date.accessioned2022-12-22T01:58:17Z-
dc.date.available2022-12-22T01:58:17Z-
dc.date.issued2022-05-
dc.identifier.issn2287-3708-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/191404-
dc.description.abstractAs the healthcare environment is being digitalized and changed rapidly, research using medical big data is increasing. One of the most applicable data is electronic medical records which can provide a large amount of clinically practical meaning. Electronic medical data include patient's demographic information, laboratory test results, imaging and biosignal data. In this article, we provide support for a wide variety of researchers in their efforts to use electronic medical record data accurately and usefully in their work. From the basic concept of the research using electronic medical records to challenging aspects like data integration between multiple institutions are described. Also, examples of each type of data are covered; structured such as numeric data and unstructured such as images, biosignals and narrative text. Using these kinds of electronic medical records, analyses are processed by data cleansing, transforming, and reducing in order. Many kinds of variables such as the exposure and outcome of interest, covariate and the research design can be chosen during the preprocessing. As many machine-learning-based studies as well as epidemiologic-based studies have been conducted using electronic medical records, various research frameworks have been proposed. However, data quality management and data standardization for multi-center data analysis are still remaining as challenging tasks.-
dc.description.statementOfResponsibilityopen-
dc.languageKorean-
dc.publisher한국보건정보통계학회-
dc.relation.isPartOfJournal of Health Informatics and Statistics(보건정보통계학회지)-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.title전자의무기록 데이터 분석 접근법-
dc.title.alternativeApproach for Electronic Medical Record Data Analysis-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Biomedical Systems Informatics (의생명시스템정보학교실)-
dc.contributor.googleauthor박찬민-
dc.contributor.googleauthor한창호-
dc.contributor.googleauthor김유정-
dc.contributor.googleauthor강소라-
dc.contributor.googleauthor박태준-
dc.contributor.googleauthor윤덕용-
dc.identifier.doi10.21032/jhis.2022.47.S1.1-
dc.contributor.localIdA06062-
dc.relation.journalcodeJ01433-
dc.subject.keywordElectronic medical records-
dc.subject.keywordData analysis-
dc.subject.keywordMachine learning-
dc.contributor.alternativeNameYoon, Dukyong-
dc.contributor.affiliatedAuthor윤덕용-
dc.citation.volume47-
dc.citation.startPageS1-
dc.citation.endPageS8-
dc.identifier.bibliographicCitationJournal of Health Informatics and Statistics (보건정보통계학회지), Vol.47 : S1-S8, 2022-05-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers

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