将开源 Gen3 平台和 kubernetes 用于 NIH HEAL IMPOWR 和 MIRHIQL 临床试验数据中心:定制、云过渡和优化。

IF 4 2区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Biomedical Informatics Pub Date : 2024-11-01 DOI:10.1016/j.jbi.2024.104749
Meredith C.B. Adams , Colin Griffin , Hunter Adams , Stephen Bryant , Robert W. Hurley , Umit Topaloglu
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引用次数: 0

摘要

目的:本研究旨在提供决策框架、策略和软件,用于利用 Gen3 平台成功部署首个慢性疼痛和阿片类药物使用数据的临床试验数据公共平台:本研究旨在提供决策框架、策略和软件,以便利用 Gen3 平台成功部署首个慢性疼痛和阿片类药物使用数据合并临床试验数据公共中心:该方法包括根据 NIH HEAL IMPOWR 和 MIRHIQL 网络的需求调整开源 Gen3 平台和 Kubernetes。关键步骤包括定制 Gen3 架构、从亚马逊云过渡到谷歌云、调整数据摄取和统一流程、确保 Kubernetes 环境的安全性和合规性,以及优化性能和用户体验:主要成果是在 Gen3 基础上建立了一个全面运行的 IMPOWR 数据中心。主要特点包括:支持多种临床试验数据类型的模块化架构、数据管理自动化流程、细粒度访问控制和审计,以及用于数据探索和分析的研究人员友好界面:维克森林 IDEA-CC 数据集的成功开发是慢性疼痛和成瘾研究的一个重要里程碑。来自不同研究的统一的 FAIR 数据可以在一个安全、可扩展的资源库中被发现。虽然在长期维护和管理方面仍存在挑战,但共享库为加速科学进步奠定了基础。获得的主要经验包括:技术专家和领域专家参与的重要性、对灵活而强大的基础设施的需求,以及在已有开源平台基础上进行构建的价值:WF IDEA-CC Gen3 数据共用区证明了为慢性疼痛和阿片类药物使用研究开发共享数据基础设施的可行性和价值。这些经验教训可为其他临床领域的类似工作提供借鉴。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Adapting the open-source Gen3 platform and kubernetes for the NIH HEAL IMPOWR and MIRHIQL clinical trial data commons: Customization, cloud transition, and optimization

Objective

This study aims to provide the decision-making framework, strategies, and software used to successfully deploy the first combined chronic pain and opioid use data clinical trial data commons using the Gen3 platform.

Materials and Methods

The approach involved adapting the open-source Gen3 platform and Kubernetes for the needs of the NIH HEAL IMPOWR and MIRHIQL networks. Key steps included customizing the Gen3 architecture, transitioning from Amazon to Google Cloud, adapting data ingestion and harmonization processes, ensuring security and compliance for the Kubernetes environment, and optimizing performance and user experience.

Results

The primary result was a fully operational IMPOWR data commons built on Gen3. Key features include a modular architecture supporting diverse clinical trial data types, automated processes for data management, fine-grained access control and auditing, and researcher-friendly interfaces for data exploration and analysis.

Discussion

The successful development of the Wake Forest IDEA-CC data commons represents a significant milestone for chronic pain and addiction research. Harmonized, FAIR data from diverse studies can be discovered in a secure, scalable repository. Challenges remain in long-term maintenance and governance, but the commons provides a foundation for accelerating scientific progress. Key lessons learned include the importance of engaging both technical and domain experts, the need for flexible yet robust infrastructure, and the value of building on established open-source platforms.

Conclusion

The WF IDEA-CC Gen3 data commons demonstrates the feasibility and value of developing a shared data infrastructure for chronic pain and opioid use research. The lessons learned can inform similar efforts in other clinical domains.
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来源期刊
Journal of Biomedical Informatics
Journal of Biomedical Informatics 医学-计算机:跨学科应用
CiteScore
8.90
自引率
6.70%
发文量
243
审稿时长
32 days
期刊介绍: The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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