Background: Electronic health records (EHRs) enable machine learning for diagnosis, prognosis, and clinical decision support. However, EHR standards vary by country and hospital, making records often incompatible. This limits large-scale and cross-clinical machine learning. To address such complexity, a metadata repository cataloguing available data elements, their value domains, and their compatibility is an essential tool. This allows researchers to leverage relevant data for tasks such as identifying undiagnosed rare disease patients. Results: Within the Screen4Care project, we developed S4CMDR, an open-source metadata repository built on ISO 11179-3, based on a middle-out metadata standardisation approach. It automates cataloguing to reduce errors and enable the discovery of compatible feature sets across data registries. S4CMDR supports on-premise Linux deployment and cloud hosting, with state-of-the-art user authentication and an accessible interface. Conclusions: S4CMDR is a clinical metadata repository registering and discovering compatible EHR records. Novel contributions include a microservice architecture, a middle-out standardisation approach, and a user-friendly interface for error-free data registration and visualisation of metadata compatibility. We validate S4CMDR's case studies involving rare disease patients. We invite clinical data holders to populate S4CMDR using their metadata to validate the generalisability and support further development.

翻译：背景：电子健康记录（EHR）能够支持诊断、预后及临床决策中的机器学习应用。然而，不同国家和医院的EHR标准存在差异，导致记录常不兼容，限制了大规模及跨临床场景的机器学习。为应对此类复杂性，一个能够编录可用数据元素、其值域及兼容性的元数据仓库是重要工具，可帮助研究人员利用相关数据完成如识别未确诊罕见病患者等任务。结果：在Screen4Care项目中，我们基于ISO 11179-3标准，采用中向元数据标准化方法，开发了开源元数据仓库S4CMDR。该系统自动执行编录操作以减少错误，并支持跨数据注册表发现兼容特征集。S4CMDR支持本地Linux部署及云端托管，配备先进用户认证机制与易用界面。结论：S4CMDR是一个用于注册和发现兼容EHR记录的临床元数据仓库。其创新贡献包括微服务架构、中向标准化方法，以及支持无错误数据注册和元数据兼容性可视化的用户友好界面。我们通过涉及罕见病患者的案例研究验证了S4CMDR的有效性。诚邀临床数据持有者使用其元数据填充S4CMDR，以验证其泛化能力并支持后续开发。