2019年
Graph databases for openEHR clinical repositories
International Journal of Computational Science and Engineering
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- 巻
- 20
- 号
- 3
- 開始ページ
- 281
- 終了ページ
- 298
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1504/IJCSE.2019.103955
- 出版者・発行元
- INDERSCIENCE ENTERPRISES LTD
The archetype-based approach has now been adopted by major EHR interoperability standards. Soon, due to an increase in EHR adoption, more health data will be created and frequently accessed. Previous research shows that conventional persistence mechanisms such as relational and XML databases have scalability issues when storing and querying archetype-based datasets. Accordingly, we need to explore and evaluate new persistence strategies for archetype-based EHR repositories. To address the performance issues expected to occur with the increase of data, we proposed an approach using labelled property graph databases for implementing openEHR clinical repositories. We implemented the proposed approach using Neo4j and compared it to an object relational mapping (ORM) approach using Microsoft SQL server. We evaluated both approaches over a simulation of a pregnancy home-monitoring application in terms of required storage space and query response time. The results show that the proposed approach provides a better overall performance for clinical querying.
- リンク情報
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- DOI
- https://doi.org/10.1504/IJCSE.2019.103955
- DBLP
- https://dblp.uni-trier.de/rec/journals/ijcse/HelouKYKKHOTK19
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000501298100001&DestApp=WOS_CPL
- URL
- https://dblp.uni-trier.de/db/journals/ijcse/ijcse20.html#HelouKYKKHOTK19
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85076222593&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85076222593&origin=inward
- ID情報
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- DOI : 10.1504/IJCSE.2019.103955
- ISSN : 1742-7185
- eISSN : 1742-7193
- DBLP ID : journals/ijcse/HelouKYKKHOTK19
- SCOPUS ID : 85076222593
- Web of Science ID : WOS:000501298100001