Clinical Data Goes MEDS? Let's OWL make sense of it

2026-01-07

arXiv.org

AI Summary

This study addresses the lack of standardization and explicit semantic representation in clinical data, which hinders interoperability and reproducibility in machine learning. To overcome this limitation, the authors propose the first integration of the MEDS clinical event model with Semantic Web technologies, resulting in MEDS-OWL—a lightweight OWL ontology comprising 13 classes, 10 object properties, 20 data properties, and 24 axioms. They further develop the meds2rdf tool to automatically transform MEDS data into FAIR-aligned RDF graphs. The approach leverages SHACL constraints for validation and RDF graph representations to provide a reusable semantic layer that enables semantic enrichment, cross-system interoperability, and graph-based analytics of clinical data. The methodology is successfully validated on a synthetic dataset capturing care pathways of patients with ruptured aneurysms.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data integration and cleaning
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The application of machine learning on healthcare data is often hindered by the lack of standardized and semantically explicit representation, leading to limited interoperability and reproducibility across datasets and experiments. The Medical Event Data Standard (MEDS) addresses these issues by introducing a minimal, event-centric data model designed for reproducible machine-learning workflows from health data. However, MEDS is defined as a data-format specification and does not natively provide integration with the Semantic Web ecosystem. In this article, we introduce MEDS-OWL, a lightweight OWL ontology that provides formal concepts and relations to represent MEDS datasets as RDF graphs. Additionally, we implemented meds2rdf, a Python conversion library that transforms MEDS events into RDF graphs, ensuring conformance with the ontology. We evaluate the proposed approach on two datasets: a synthetic clinical cohort describing care pathways for ruptured intracranial aneurysms, and a real-world subset of MIMIC-IV. To assess semantic consistency, we performed a SHACL validation against the resulting knowledge graphs. The first release of MEDS-OWL comprises 13 classes, 10 object properties, 20 data properties, and 24 OWL axioms. Combined with meds2rdf, it enables data transformation into FAIR-aligned datasets, provenance-aware publishing, and interoperability of event-based clinical data. By bridging MEDS with the Semantic Web, this work contributes a reusable semantic layer for event-based clinical data and establishes a robust foundation for subsequent graph-based analytics.

Problem

• clinical data
• semantic interoperability
• MEDS
• Semantic Web
• FAIR data

Innovation

• MEDS-OWL
• Semantic Web
• RDF
• FAIR data
• clinical event data