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Semantic processing of EHR data for clinical research
Open accessAbstract
There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.
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“A major challenge in using more of the data available for each patient has been the lack of standards and semantic interoperability of health data from multiple sites.25”
From Scalable and accurate deep learning with electronic health records (2018) · cites this paper as [25]
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Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648
PIC, a paediatric-specific intensive care database
Zeng, X., Yu, G., Lu, Y., Tan, L., Wu, X., Shi, S., Duan, H., Shu, Q., & Li, H. (2020) · Scientific Data · doi:10.1038/s41597-020-0355-4 · Cited by 138
Metadata
- Authors
- Sun, H., Depraetere, K., De Roo, J., Mels, G., De Vloed, B., Twagirumukiza, M., & Colaert, D.
- Affiliations
- Agfa HealthCare (BE)
- Corresponding author
- Sun, Hong
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 58
- Pages
- 247-259
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2146958003
- Cited by
- 56
- References
- 0
- Citations by year
- 2025: 2 · 2024: 7 · 2023: 2 · 2022: 9 · 2021: 8 · 2020: 10 · 2019: 4 · 2018: 7 · 2017: 3 · 2016: 3 · 2015: 1
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- SALUS (287800)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- electronic health records, semantic data integration, data virtualization, RDF, domain ontology, terminology, i2b2, OMOP, clinical research databases, heterogeneous data sources, proof generation
- MeSH terms
- Semantics, Biomedical Research, Electronic Health Records
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