ArticleScientific data2024
Cross-Standard Health Data Harmonization using Semantics of Data Elements.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Towards a Compositional Framework for Describing Human Phenotypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Current status and future directions for the use of artificial intelligence in pediatric critical care nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Enhancing semantic interoperability in precision medicine: converting OMOP CDM to Beacon v2 in the Spanish IMPaCT-Data project.BMC medical informatics and decision making · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- PANCDetect: Early Detection of Pancreatic Cancer from Multimodal EHR data with LLM Embeddings.medRxiv : the preprint server for health sciences · 2025Article
- A General-Purpose Data Harmonization Framework: Supporting Reproducible and Scalable Data Integration in the RADx Data Hub.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Faced with heterogeneity of healthcare data, we propose a novel approach for harmonizing data elements (i.e., attributes) across health data standards. This approach focuses on the implicit concept that is represented by a data element. The process includes the following steps: identifying concepts, clustering similar concepts and constructing mappings between the clusters using the Simple Standard for Sharing Ontological Mappings (SSSOM) and Resource Description Framework (RDF), and enabling the creation of reusable mappings. As proof-of-concept, we applied the approach to five common health data standards - HL7 FHIR, OMOP, CDISC, Phenopackets, and openEHR, across four domains, such as demographics and diagnoses, and nine topics within those domains, such as gender and vital status. These domains and topics are selected to represent the broader range of topics in the health field. For each topic, data elements were found in the health data standards after a thorough search, resulting in the analysis of 64 data elements, identification of their underlying concepts, and development of mappings. Three use cases were implemented to demonstrate the role of data element concepts in data harmonization and data querying at varying levels of granularity. The approach helps overcome the limitations of context-dependent mappings and provides valuable insight for mapping practice within the health domain.
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What Socratic holds
Registered trials
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 Socratic graph.