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将数据来源嵌入学习型健康系统以促进可重复研究。

Embedding data provenance into the Learning Health System to facilitate reproducible research.

作者信息

Curcin Vasa

机构信息

Division of Health and Social Care Research King's College London London UK.

Department of Informatics King's College London London UK.

出版信息

Learn Health Syst. 2016 Dec 27;1(2):e10019. doi: 10.1002/lrh2.10019. eCollection 2017 Apr.

Abstract

INTRODUCTION

The learning health system (LHS) community has taken up the challenge of bringing the complex relationship between clinical research and practice into this brave new world. At the heart of the LHS vision is the notion of routine capture, transformation, and dissemination of data and knowledge, with various use cases, such as clinical studies, quality improvement initiatives, and decision support, constructed on top of specific routes that the data is taking through the system. In order to stop this increased data volume and analytical complexity from obfuscating the research process, it is essential to establish trust in the system through implementing reproducibility and auditability throughout the workflow.

METHODS

Data provenance technologies can automatically capture the trace of the research task and resulting data, thereby facilitating reproducible research. While some computational domains, such as bioinformatics, have embraced the technology through provenance-enabled execution middlewares, disciplines based on distributed, heterogeneous software, such as medical research, are only starting on the road to adoption, motivated by the institutional pressures to improve transparency and reproducibility.

RESULTS

Guided by the experiences of the TRANSFoRm project, we present the opportunities that data provenance offers to the LHS community. We illustrate how provenance can facilitate documenting 21 CFR Part 11 compliance for Food and Drug Administration submissions and provide auditability for decisions made by the decision support tools and discuss the transformational effect of routine provenance capture on data privacy, study reporting, and publishing medical research.

CONCLUSIONS

If the scaling up of the LHS is to succeed, we have to embed mechanisms to verify trust in the system inside our research instruments. In the research world increasingly reliant on electronic tools, provenance gives us a lingua franca to achieve traceability, which we have shown to be essential to building these mechanisms. To realize the vision of making computable provenance a feasible approach to implementing reproducibility in the LHS, we have to provide viable mechanisms for adoption. These include defining meaningful provenance models for problem domains and also introducing provenance support to existing tools in a minimally invasive manner.

摘要

引言

学习型健康系统(LHS)社区已接受挑战,将临床研究与实践之间的复杂关系引入这个全新的世界。LHS愿景的核心是数据和知识的常规捕获、转换及传播概念,在数据流经系统的特定路径之上构建了各种用例,如临床研究、质量改进计划和决策支持。为防止数据量增加和分析复杂性掩盖研究过程,必须通过在整个工作流程中实施可重复性和可审计性来建立对系统的信任。

方法

数据溯源技术可自动捕获研究任务及结果数据的踪迹,从而促进可重复研究。虽然一些计算领域,如生物信息学,已通过启用溯源的执行中间件采用了该技术,但基于分布式、异构软件的学科,如医学研究,受提高透明度和可重复性的机构压力推动,才刚刚踏上采用之路。

结果

以TRANSFoRm项目的经验为指导,我们展示了数据溯源为LHS社区带来的机遇。我们说明了溯源如何有助于记录向美国食品药品监督管理局提交材料时符合21 CFR Part 11的情况,并为决策支持工具所做的决策提供可审计性,还讨论了常规溯源捕获对数据隐私、研究报告和医学研究发表的变革性影响。

结论

如果LHS的扩大规模要取得成功,我们必须在研究工具中嵌入验证对系统信任的机制。在日益依赖电子工具的研究领域,溯源为我们提供了一种通用语言来实现可追溯性,我们已证明这对于构建这些机制至关重要。为实现使可计算溯源成为在LHS中实施可重复性的可行方法这一愿景,我们必须提供可行的采用机制。这些机制包括为问题领域定义有意义的溯源模型,以及以微创方式将溯源支持引入现有工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d53/6516719/3516769951c8/LRH2-1-e10019-g001.jpg

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