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 enable representing 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 demonstrate the approach on a synthetic clinical dataset that describes patient care pathways for ruptured intracranial aneurysms and validate the resulting graph using SHACL constraints. 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.

翻译：在医疗健康数据上应用机器学习常因缺乏标准化和语义明确表示而受阻，导致数据集与实验间的互操作性和可复现性受限。医学事件数据标准（MEDS）通过引入一个极简的、以事件为中心的数据模型来解决这些问题，该模型专为从健康数据中实现可复现的机器学习工作流而设计。然而，MEDS被定义为数据格式规范，本身未提供与语义网生态系统的集成。本文介绍MEDS-OWL——一个轻量级的OWL本体，它提供形式化概念与关系，使得将MEDS数据集表示为RDF图成为可能。此外，我们实现了meds2rdf，一个Python转换库，可将MEDS事件转化为RDF图，并确保其符合本体规范。我们在一个描述颅内动脉瘤破裂患者护理路径的合成临床数据集上演示了该方法，并使用SHACL约束对生成的图进行了验证。MEDS-OWL的首个版本包含13个类、10个对象属性、20个数据属性和24条OWL公理。结合meds2rdf，它能够将数据转化为符合FAIR原则的数据集，支持可溯源的发布以及基于事件的临床数据的互操作。通过连接MEDS与语义网，本研究为基于事件的临床数据贡献了一个可复用的语义层，并为后续基于图的分析奠定了坚实基础。