将医疗事件数据标准MEDS转化为可互操作的语义网格式。
Clinical Data Goes MEDS? Let's OWL make sense of it
- 构建MEDS-OWL轻量级本体，用RDF图表示医疗事件数据。
- 在合成与真实数据集上验证知识图谱语义一致性，符合SHACL规范。
- 适合需要数据可追溯、跨系统共享的临床研究者使用。
机器学习在医疗数据中的应用常受限于缺乏标准化和语义明确的数据表示，导致数据集间互操作性与实验可复现性差。医学事件数据标准（MEDS）通过事件驱动的最小化数据模型，支持可复现的机器学习流程。但MEDS仅为数据格式规范，未原生集成到语义网生态中。本文提出MEDS-OWL，一个轻量级OWL本体，用于将MEDS数据表示为RDF图。同时实现meds2rdf Python库，将MEDS事件转换为符合本体的RDF图。在两个数据集上评估：一个描述破裂颅内动脉瘤护理路径的合成队列，以及MIMIC-IV的真实子集。通过SHACL验证知识图谱的语义一致性。MEDS-OWL首版包含13个类、10个对象属性、20个数据属性和24条OWL公理。结合meds2rdf，实现符合FAIR原则的数据集、带溯源的发布及事件型临床数据的互操作。该工作将MEDS与语义网连接，为事件型临床数据提供可复用的语义层，奠定图分析基础。
原文摘要 · Abstract (English)
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.
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