语义网技术存储电子健康记录表型算法的可计算定义的评价
Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms
摘要：
电子健康记录是在常规患者护理期间或作为其副产品生成的电子数据。结构化、半结构化和非结构化EHR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方法的发展。EHR的一个主要用例是定义识别疾病状态、发作和严重程度的表型算法。表型分析算法利用诊断、处方、实验室测试、症状和其他元素来识别具有或不具有特定特征的患者。不存在用于存储表型分型算法的通用标准化、结构化、可计算格式。大多数算法存储为人类可读的描述性文本文档，由于其固有的复杂性，将其翻译为代码具有挑战性，并阻碍了其在社区中的共享和重用。在本文中，我们评估的两个关键的语义Web技术，Web本体语言和资源描述框架，使可计算表示的EHR驱动的表型分析算法。
英文摘要：
Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
