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Evaluating OpenEHR for Storing Computable Representations of Electronic Health Record Phenotyping Algorithms

机译:评估OpenEHR以存储电子病历表型算法的可计算表示形式

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Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.
机译:电子健康记录(EHR)是常规临床护理期间生成的数据。电子病历为研究人员提供了前所未有的表型广度和深度,并有可能加速大规模精准医学的发展。 EHR的主要用例是创建表型算法,以定义疾病状态,发作和严重性。当前,不存在用于定义通常以人类可读格式存储的表型算法的通用机器可读标准。结果,将算法转换为实现代码具有挑战性,并且在整个科学界共享问题。在本文中,我们评估了正式的EHR数据规范openEHR,用于EHR表型算法的可计算表示。

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