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 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.
Abstract (translated)
在医疗数据上应用机器学习往往因缺乏标准化和语义明确的数据表示而受阻，导致跨数据集和实验的互操作性和可重复性有限。《医学事件数据标准》（MEDS）通过引入一个最小化、以事件为中心的数据模型来解决这些问题，该模型旨在从健康数据中实现可重复的机器学习工作流程。然而，MEDS仅被定义为一种数据格式规范，并未原生提供与语义网生态系统的集成。本文介绍了MEDS-OWL，这是一种轻量级的OWL本体，提供了正式的概念和关系，以支持将MEDS数据集表示为RDF图。此外，我们实现了meds2rdf，这是一个Python转换库，能够将MEDS事件转换成符合该本体规范的RDF图。 我们在一个合成临床数据集上展示了这种方法的应用，该数据集描述了颅内动脉瘤破裂后的患者治疗路径，并通过SHACL约束验证了生成的图。MEDS-OWL的第一个版本包括13个类、10个对象属性、20个数据属性和24条OWL公理。 结合meds2rdf使用时，它能够将数据转换成与FAIR原则对齐的数据集，支持带有出处意识的数据发布，并促进基于事件的临床数据之间的互操作性。通过将MEDS与语义网连接起来，这项工作提供了一个可重用的基于事件的临床数据语义层，并为后续图基分析奠定了坚实的基础。
URL
https://arxiv.org/abs/2601.04164
