Adams Meredith C B, Griffin Colin, Adams Hunter, Bryant Stephen, Hurley Robert W, Topaloglu Umit
Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, NC, United States.
Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, United States.
Front Digit Health. 2025 Jul 23;7:1570009. doi: 10.3389/fdgth.2025.1570009. eCollection 2025.
This work presents a framework for enhancing Gen3, an open-source data commons platform, with temporal visualization capabilities for clinical trial research. We describe the technical implementation of cloud-native architecture and integrated visualization tools that enable standardized analytics for longitudinal clinical trial data while adhering to FAIR principles. The enhancement includes Kubernetes-based container orchestration, Kibana-based temporal analytics, and automated ETL pipelines for data harmonization. Technical validation demonstrates reliable handling of varied time-based data structures, while maintaining temporal precision and measurement context. The framework's implementation in NIH HEAL Initiative networks studying chronic pain and substance use disorders showcases its utility for real-time monitoring of longitudinal outcomes across multiple trials. This adaptation provides a model for research networks seeking to enhance their data commons capabilities while ensuring findable, accessible, interoperable, and reusable clinical trial data.
这项工作提出了一个框架，用于增强Gen3（一个开源数据共享平台），使其具备用于临床试验研究的时间可视化功能。我们描述了云原生架构和集成可视化工具的技术实现，这些工具能够在遵循FAIR原则的同时，对纵向临床试验数据进行标准化分析。增强功能包括基于Kubernetes的容器编排、基于Kibana的时间分析以及用于数据协调的自动化ETL管道。技术验证表明，该框架能够可靠地处理各种基于时间的数据结构，同时保持时间精度和测量上下文。该框架在国立卫生研究院（NIH）针对慢性疼痛和物质使用障碍的HEAL计划网络中的实施，展示了其在多个试验中对纵向结果进行实时监测的效用。这种改编为寻求增强其数据共享能力，同时确保临床试验数据可查找、可访问、可互操作和可重复使用的研究网络提供了一个模型。
