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首页> 外文会议>International Conference on Bioinformatics and Biomedicine Workshops >A Proposed Star Schema and Extraction Process to Enhance the Collection of Contextual Semantic Information for Clinical Research Data Warehouses
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A Proposed Star Schema and Extraction Process to Enhance the Collection of Contextual Semantic Information for Clinical Research Data Warehouses

机译：提出的星形图谱和提取过程，以增强临床研究数据仓库的上下文和语义信息的集合

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In the past decade, clinical patient data has played a pivotal role in clinical and translational research in support of new treatment options, medical interventions, drug development, etc. In support of this process, researchers require massive integrated data sets generated via a health information exchange (HIE) to centralize and automate the development and maintenance of a clinical research data warehouse (CRDW). The data harvested from the CRDW is obtained by cleansing transactional clinical databases (TCD) used for daily clinical activities. Traditionally, TCD schema and CRDW data models only capture conceptual patient data, often neglecting to address the contextual and semantic information attached to such data that is crucial for clinical analysis. In this paper, we propose a star schema and associated extraction process to enhance the collection of contextual and semantic information in support of CRDW that leverages HL7 Clinical Document. Architecture in conjunction with the Reference Information Model.
机译：在过去的十年中，临床患者数据在临床和翻译研究中发挥了关键作用，支持新的治疗方案，医疗干预，药物开发等。支持这一过程，研究人员需要通过健康信息产生的大规模集成数据集Exchange（HIE）集中和自动化临床研究数据仓库（CRDW）的开发和维护。通过清洁用于日常临床活动的事务临床数据库（TCD）获得从CRDW收获的数据。传统上，TCD架构和CRDW数据模型仅捕获概念性患者数据，通常忽略以解决对临床分析至关重要的这些数据的上下文和语义信息。在本文中，我们提出了一个星型模式和相关的提取工艺，以提高语境和语义信息的收集，以支持CRDW的，它利用HL7临床文档。架构与参考信息模型相结合。

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