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

TL;DR

This paper introduces MEDS-OWL, an OWL ontology and conversion tool that semantically enrich clinical event data, enhancing interoperability, FAIR compliance, and enabling advanced graph-based healthcare analytics.

Contribution

It provides a formal OWL ontology and a Python library to convert MEDS datasets into RDF graphs, bridging clinical data standards with the Semantic Web ecosystem.

Findings

Successfully implemented MEDS-OWL with 13 classes and 24 axioms.

Validated RDF graphs with SHACL for semantic consistency.

Enabled FAIR-aligned, provenance-aware clinical data transformation.

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.