Dubovitskaya Alevtina, Urovi Visara, Barba Imanol, Aberer Karl, Schumacher Michael Ignaz
Applied Intelligent Systems Laboratory, HES-SO VS, Sierre, Switzerland; Distributed Information Systems Laboratory, EPFL, Lausanne, Switzerland.
Accounting and Information Management, Maastricht University, Maastricht, Netherlands.
Biomed Res Int. 2016;2016:9027457. doi: 10.1155/2016/9027457. Epub 2016 Nov 16.
The collection of medical data for research purposes is a challenging and long-lasting process. In an effort to accelerate and facilitate this process we propose a new framework for dynamic aggregation of medical data from distributed sources. We use agent-based coordination between medical and research institutions. Our system employs principles of peer-to-peer network organization and coordination models to search over already constructed distributed databases and to identify the potential contributors when a new database has to be built. Our framework takes into account both the requirements of a research study and current data availability. This leads to better definition of database characteristics such as schema, content, and privacy parameters. We show that this approach enables a more efficient way to collect data for medical research.
出于研究目的收集医学数据是一个具有挑战性且持久的过程。为了加速并促进这一过程,我们提出了一个用于动态聚合来自分布式源的医学数据的新框架。我们使用医学机构与研究机构之间基于代理的协调方式。我们的系统采用对等网络组织和协调模型的原则,在已构建的分布式数据库中进行搜索,并在需要构建新数据库时识别潜在的贡献者。我们的框架既考虑了研究的要求,也考虑了当前的数据可用性。这使得能够更好地定义数据库特征,如图式、内容和隐私参数。我们表明,这种方法能够以更高效的方式为医学研究收集数据。