Desiderata for computable representations of electronic health records-driven phenotype algorithms.
Desiderata for computable representations of electronic health records-driven phenotype algorithms.
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电子健康记录驱动的表型算法的可计算表示的Desiderata。
DOI：
10.1093/jamia/ocv112
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Harris PA
中科院分区：
文献类型：
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作者：
Mo H;Thompson WK;Rasmussen LV;Pacheco JA;Jiang G;Kiefer R;Zhu Q;Xu J;Montague E;Carrell DS;Lingren T;Mentch FD;Ni Y;Wehbe FH;Peissig PL;Tromp G;Larson EB;Chute CG;Pathak J;Denny JC;Speltz P;Kho AN;Jarvik GP;Bejan CA;Williams MS;Borthwick K;Kitchner TE;Roden DM;Harris PA
背景通过表型算法的创建，电子健康记录(EHR)越来越多地被用于临床和翻译研究。目前，表型算法最常见的表示为不可计算的描述性文档和知识人工制品，其详细描述了用于查询诊断、症状、过程、药物和/或文本驱动的医学概念的协议，并且主要用于人类理解。我们提出了开发可计算的表型表示模型(PheRM)的期望。方法一个由临床医生和信息学家组成的团队回顾了发表在PheKB.org和现有表型表示平台上的多点表型算法的共同特征。我们还评估了众所周知的诊断标准和临床决策指南，以涵盖更广泛的算法类别。结果我们提出了一个灵活的、可计算的PheRM所需的10个特征：(1)将临床数据组织成可查询的形式；(2)推荐使用公共数据模型，但也支持定制站点间EHR数据的可变性和可用性；(3)支持人类可读和可计算的表型算法表示；(4)实现集合运算和关系代数来建模表型算法；(5)用结构化规则表示表型标准；(6)支持定义事件之间的时间关系；(7)使用标准化的术语和本体，并促进值集的重用；(8)定义文本搜索和自然语言处理的表示；(9)为外部软件算法提供接口；以及(10)保持向后兼容性。结论：为了在不同的EHR产品和医疗保健系统中实现真正的表型、可移植性和可靠性，需要一个可计算的PheRM。这些期望数据是指导EHR表型算法创作平台和语言的建立和发展的指南。
Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.