Data Provenance in Healthcare: Approaches, Challenges, and Future Directions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahmed, Mansoor | - |
dc.contributor.author | Dar, Amil Rohani | - |
dc.contributor.author | Helfert, Markus | - |
dc.contributor.author | Khan, Abid | - |
dc.contributor.author | Kim, Jungsuk | - |
dc.date.accessioned | 2023-08-28T00:41:50Z | - |
dc.date.available | 2023-08-28T00:41:50Z | - |
dc.date.created | 2023-08-25 | - |
dc.date.issued | 2023-07 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88887 | - |
dc.description.abstract | Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that make it possible to track the sources and reasons behind any problem with a user's data. With the emergence of the General Data Protection Regulation (GDPR), data provenance in healthcare systems should be implemented to give users more control over data. This SLR studies the impacts of data provenance in healthcare and GDPR-compliance-based data provenance through a systematic review of peer-reviewed articles. The SLR discusses the technologies used to achieve data provenance and various methodologies to achieve data provenance. We then explore different technologies that are applied in the healthcare domain and how they achieve data provenance. In the end, we have identified key research gaps followed by future research directions. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | Data Provenance in Healthcare: Approaches, Challenges, and Future Directions | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 001039028200001 | - |
dc.identifier.doi | 10.3390/s23146495 | - |
dc.identifier.bibliographicCitation | SENSORS, v.23, no.14 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85165991633 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 23 | - |
dc.citation.number | 14 | - |
dc.contributor.affiliatedAuthor | Kim, Jungsuk | - |
dc.type.docType | Review | - |
dc.subject.keywordAuthor | data provenance | - |
dc.subject.keywordAuthor | healthcare | - |
dc.subject.keywordAuthor | provenance technologies | - |
dc.subject.keywordAuthor | cryptography | - |
dc.subject.keywordAuthor | ontologies | - |
dc.subject.keywordAuthor | blockchain | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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