PDF(Pubmed)
Abstract：
The development of platforms for distributed analytics has been driven by a growing need to comply with various governance-related or legal constraints. Among these platforms, the so-called Personal Health Train (PHT) is one representative that has emerged over the recent years. However, in projects that require data from sites featuring different PHT infrastructures, institutions are facing challenges emerging from the combination of multiple PHT ecosystems, including data governance, regulatory compliance, or the modification of existing workflows. In these scenarios, the interoperability of the platforms is preferable. In this work, we introduce a conceptual framework for the technical interoperability of the PHT covering five essential requirements: Data integration, unified station identifiers, mutual metadata, aligned security protocols, and business logic. We evaluated our concept in a feasibility study that involves two distinct PHT infrastructures: PHT-meDIC and PADME. We analyzed data on leukodystrophy from patients in the University Hospitals of Tübingen and Leipzig, and patients with differential diagnoses at the University Hospital Aachen. The results of our study demonstrate the technical interoperability between these two PHT infrastructures, allowing researchers to perform analyses across the participating institutions. Our method is more space-efficient compared to the multi-homing strategy, and it shows only a minimal time overhead.
摘要：
分布式分析平台的开发是由满足各种治理相关或法律约束的日益增长的需求推动的。在这些平台中,所谓的个人健康培训（PHT）是近年来出现的代表之一。然而,在需要来自具有不同PHT基础设施的站点的数据的项目中，机构面临着多种PHT生态系统组合带来的挑战，包括数据治理，法规遵从性，或修改现有工作流。在这些场景中,平台的互操作性更好。在这项工作中,我们介绍了PHT技术互操作性的概念框架，涵盖了五个基本要求：数据集成，统一车站标识符，相互元数据，一致的安全协议，商业逻辑。我们在一项可行性研究中评估了我们的概念，该研究涉及两个不同的PHT基础设施：PHT-meDIC和PADME。我们分析了蒂宾根大学医院和莱比锡大学医院患者的脑白质营养不良数据，和亚琛大学医院的鉴别诊断患者。我们的研究结果证明了这两个PHT基础设施之间的技术互操作性，允许研究人员在参与机构中进行分析。与多归属策略相比，我们的方法更节省空间，它只显示了一个最小的时间开销。
