ArticleBMC medical informatics and decision making2020
Composite CDE: modeling composite relationships between common data elements for representing complex clinical data.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A CDE-based data structure for radiotherapeutic decision-making in breast cancer.BMC medical informatics and decision making · 2025Article
- A community-engaged approach to developing common data elements: a case study from the RADx-UP Long COVID common data elements Task Force.JAMIA open · 2025Article
- An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets.Scientific data · 2025Article
- Integrating community perspectives to improve survey completion rates in public health research by refining controversial survey elements.Journal of clinical and translational science · 2025Article
- Toward Better Semantic Interoperability of Data Element Repositories in Medicine: Analysis Study.JMIR medical informatics · 2024Article
- How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.Orphanet journal of rare diseases · 2024Article
- Standardizing, harmonizing, and protecting data collection to broaden the impact of COVID-19 research: the rapid acceleration of diagnostics-underserved populations (RADx-UP) initiative.Journal of the American Medical Informatics Association : JAMIA · 2022Review
- Harmonization and standardization of data for a pan-European cohort on SARS- CoV-2 pandemic.NPJ digital medicine · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundSemantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical data elements (DEs). However, the standard model has both structural and semantic limitations, and the number of DEs continues to increase due to poor term reusability. Semantic types and constraints are lacking for comprehensively describing and evaluating DEs on real-world clinical documents.
methodsWe addressed these limitations by defining three new types of semantic relationship (dependency, composite, and variable) in our previous studies. The present study created new and further extended existing semantic types (hybrid atomic and repeated and dictionary composite common data elements [CDEs]) with four constraints: ordered, operated, required, and dependent. For evaluation, we extracted all atomic and composite CDEs from five major clinical documents from five teaching hospitals in Korea, 14 Fast Healthcare Interoperability Resources (FHIR) resources from FHIR bulk sample data, and MIMIC-III (Medical Information Mart for Intensive Care) demo dataset. Metadata reusability and semantic interoperability in real clinical settings were comprehensively evaluated by applying the CDEs with our extended semantic types and constraints.
resultsAll of the CDEs (n = 1142) extracted from the 25 clinical documents were successfully integrated with a very high CDE reuse ratio (46.9%) into 586 CDEs (259 atomic and 20 unique composite CDEs), and all of CDEs (n = 238) extracted from the 14 FHIR resources of FHIR bulk sample data were successfully integrated with high CDE reuse ration (59.7%) into 96 CDEs (21 atomic and 28 unique composite CDEs), which improved the semantic integrity and interoperability without any semantic loss. Moreover, the most complex data structures from two CDE projects were successfully encoded with rich semantics and semantic integrity.
conclusionMDR-based extended semantic types and constraints can facilitate comprehensive representation of clinical documents with rich semantics, and improved semantic interoperability without semantic loss.
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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.