The 2011-2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance
DC Field | Value | Language |
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dc.contributor.author | Shin, Hyunah | - |
dc.contributor.author | Cha, Jaehun | - |
dc.contributor.author | Lee, Chungchun | - |
dc.contributor.author | Song, Hyejin | - |
dc.contributor.author | Jeong, Hyuntae | - |
dc.contributor.author | Kim, Jong-Yeup | - |
dc.contributor.author | Lee, Suehyun | - |
dc.date.accessioned | 2023-06-17T08:42:21Z | - |
dc.date.available | 2023-06-17T08:42:21Z | - |
dc.date.created | 2023-06-17 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88138 | - |
dc.description.abstract | Pharmacovigilance, the scientific discipline pertaining to drug safety, has been studied extensively and is progressing continuously. In this field, medical informatics techniques and interpretation play important roles, and appropriate approaches are required. In this study, we investigated and analyzed the trends of pharmacovigilance systems, especially the data collection, detection, assessment, and monitoring processes. We used PubMed to collect papers on pharmacovigilance published over the past 10 years, and analyzed a total of 40 significant papers to determine the characteristics of the databases and data analysis methods used to identify drug safety indicators. Through systematic reviews, we identified the difficulty of standardizing data and terminology and establishing an adverse drug reactions (ADR) evaluation system in pharmacovigilance, and their corresponding implications. We found that appropriate methods and guidelines for active pharmacovigilance using medical big data are still required and should continue to be developed. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.title | The 2011-2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000627997200001 | - |
dc.identifier.doi | 10.3390/app11052249 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.11, no.5, pp.1 - 13 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85102575609 | - |
dc.citation.endPage | 13 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 11 | - |
dc.citation.number | 5 | - |
dc.contributor.affiliatedAuthor | Lee, Suehyun | - |
dc.type.docType | Review | - |
dc.subject.keywordAuthor | systematic review | - |
dc.subject.keywordAuthor | data-driven approach | - |
dc.subject.keywordAuthor | pharmacovigilance | - |
dc.subject.keywordAuthor | medical big data | - |
dc.subject.keywordAuthor | adverse drug reaction | - |
dc.subject.keywordAuthor | medical informatics | - |
dc.subject.keywordPlus | ADVERSE DRUG-REACTIONS | - |
dc.subject.keywordPlus | HEALTH-CARE RECORDS | - |
dc.subject.keywordPlus | SAFETY | - |
dc.subject.keywordPlus | SIGNAL | - |
dc.subject.keywordPlus | PHARMACOEPIDEMIOLOGY | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordPlus | QUALITY | - |
dc.subject.keywordPlus | PROJECT | - |
dc.subject.keywordPlus | EVENTS | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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