ArticleJournal of the American Medical Informatics Association : JAMIA2015
Transformation of standardized clinical models based on OWL technologies: from CEM to OpenEHR archetypes.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Optimizing patient check-in process for telehealth visits: a data-driven perspective.Frontiers in digital health · 2025Article
- Operational tolerance research in liver transplantation: a bibliometric analysis using a new web resource.Frontiers in research metrics and analytics · 2024Article
- Semantic interoperability in health records standards: a systematic literature review.Health and technology · 2022Review
- Design and Construction of a NLP Based Knowledge Extraction Methodology in the Medical Domain Applied to Clinical Information.Healthcare informatics research · 2018Article
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Design of an extensive information representation scheme for clinical narratives.Journal of biomedical semantics · 2017Article
- S2O - A software tool for integrating research data from general purpose statistic software into electronic data capture systems.BMC medical informatics and decision making · 2017Article
- Generation of open biomedical datasets through ontology-driven transformation and integration processes.Journal of biomedical semantics · 2016Article
- A platform for exploration into chaining of web services for clinical data transformation and reasoning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Health information technology data standards get down to business: maturation within domains and the emergence of interoperability.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- AMIA--Setting the Standard.Journal of the American Medical Informatics Association : JAMIA · 2015Article
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Authors and funding
5 authors.
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Abstract
introductionThe semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. International initiatives pursue the use of semantically interoperable clinical models, and ontologies have frequently been used in semantic interoperability efforts. The objective of this paper is to propose a generic, ontology-based, flexible approach for supporting the automatic transformation of clinical models, which is illustrated for the transformation of Clinical Element Models (CEMs) into openEHR archetypes.
methodsOur transformation method exploits the fact that the information models of the most relevant EHR specifications are available in the Web Ontology Language (OWL). The transformation approach is based on defining mappings between those ontological structures. We propose a way in which CEM entities can be transformed into openEHR by using transformation templates and OWL as common representation formalism. The transformation architecture exploits the reasoning and inferencing capabilities of OWL technologies.
resultsWe have devised a generic, flexible approach for the transformation of clinical models, implemented for the unidirectional transformation from CEM to openEHR, a series of reusable transformation templates, a proof-of-concept implementation, and a set of openEHR archetypes that validate the methodological approach.
conclusionsWe have been able to transform CEM into archetypes in an automatic, flexible, reusable transformation approach that could be extended to other clinical model specifications. We exploit the potential of OWL technologies for supporting the transformation process. We believe that our approach could be useful for international efforts in the area of semantic interoperability of EHR systems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.