首页> 美国卫生研究院文献>Journal of the American Medical Informatics Association : JAMIA >A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research
【2h】

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research

机译:建立分布式多元模型并管理不同数据共享策略的系统:在可扩展的国家网络中实施以进行有效性研究

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

>Background Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner.>Objective The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies.>Materials and Methods Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network.>Results The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws.>Discussion and Conclusion Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.
机译:>背景目前存在用于研究网络中数据共享的集中式和联合模型。为了建立用于集中式网络的多元数据分析,必须将患者级别的数据传输到中央计算资源。作者为联邦网络实现了分布式多变量模型,在该模型中,每个站点都保存了患者级别的数据,并且以研究为中心管理数据交换策略。>目标,目标是实现支持基础架构的基础架构。一些现有研究网络的功能(例如,队列发现,工作流管理以及对集中式数据的多元分析模型的估计),同时添加了其他重要的新功能,例如分布式迭代多元模型的算法,多元模型规范的图形界面,同步以及对网络查询,研究者发起的研究以及对研究人员,协议和数据共享策略的基于研究的控制的异步响应。>材料和方法基于从统计人员,管理员和研究人员那里收集的要求在多个机构中,作者开发了支持多人的基础设施和工具使用Web服务对SCANNER联邦网络中的多元统计估计进行isite比较有效性研究。>结果作者实施了大规模并行(map-reduce)计算方法和新的策略管理系统,以使每项研究都能由网络发起参与者定义可以处理,管理,查询和共享数据的方式。作者说明了这些系统在政策差异很大且在不同州法律下运行的机构之间的使用。>讨论和结论联合研究网络不必限制分布式查询功能来对查询,队列发现或独立估算进行计数分析模型。无需患者级别的数据传输,就可以高效,安全地进行多变量分析,从而使对本地数据存储有严格要求的机构可以参与基于联合研究网络的复杂分析。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号