Despite the widespread use of the “Informatics for Integrating Biology and the Bedside” (i2b2) platform, there are substantial challenges for loading electronic health records (EHR) into i2b2 and for querying i2b2. We have previously presented a simplified framework for semantic abstraction of EHR records into i2b2. Building on our previous work, we have created a proof-of-concept implementation of cloud services on an i2b2 data store for cohort identification. Specifically, we have implemented a graphical user interface (GUI) that declares the key components for data import, transformation, and query of EHR data. The GUI integrates with Azure cloud services to create data pipelines for importing EHR data into i2b2, creation of derived facts, and querying for generating Sankey-like flow diagrams that characterize the patient cohorts. We have evaluated the implementation using the real-world MIMIC-III dataset. We discuss the key features of this implementation and direction for future work, which will advance the efforts of the research community for patient cohort identification.

中文翻译：

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使用用于整合生物学和床头平台的信息学进行患者队列识别的云服务。

尽管“整合生物学和病床的信息学”（i2b2）平台得到了广泛使用，但将电子健康记录（EHR）加载到i2b2以及查询i2b2仍存在巨大挑战。之前，我们已经提供了一个简化的框架，用于将EHR记录语义抽象到i2b2。在我们之前的工作的基础上，我们已经在i2b2数据存储上创建了云服务的概念验证实现，用于队列识别。具体来说，我们已经实现了图形用户界面（GUI），该界面声明了用于数据导入，转换和查询EHR数据的关键组件。该GUI与Azure云服务集成在一起，以创建数据管道，以将EHR数据导入i2b2，创建派生事实，并进行查询以生成可表征患者队列的类似Sankey的流程图。我们已经使用实际的MIMIC-III数据集评估了实现。我们将讨论此实现的关键特征以及未来工作的方向，这将推动研究团体在患者队列识别方面的努力。