Popovic Jennifer R
Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
Ann N Y Acad Sci. 2017 Jan;1387(1):105-111. doi: 10.1111/nyas.13287. Epub 2016 Nov 18.
This paper defines the attributes of distributed data networks and outlines the data and analytic infrastructure needed to build and maintain a successful network. We use examples from one successful implementation of a large-scale, multisite, healthcare-related distributed data network, the U.S. Food and Drug Administration-sponsored Sentinel Initiative. Analytic infrastructure-development concepts are discussed from the perspective of promoting six pillars of analytic infrastructure: consistency, reusability, flexibility, scalability, transparency, and reproducibility. This paper also introduces one use case for machine learning algorithm development to fully utilize and advance the portfolio of population health analytics, particularly those using multisite administrative data sources.
本文定义了分布式数据网络的属性，并概述了构建和维护成功网络所需的数据及分析基础设施。我们使用了一个大规模、多站点、与医疗保健相关的分布式数据网络成功实施案例中的示例，即美国食品药品监督管理局赞助的哨兵计划。从促进分析基础设施的六个支柱（一致性、可重用性、灵活性、可扩展性、透明度和可重复性）的角度讨论了分析基础设施开发概念。本文还介绍了一个机器学习算法开发的用例，以充分利用并推进人群健康分析组合，特别是那些使用多站点行政数据源的分析。
