PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability
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DOI：
10.1093/jamia/ocv202
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发表时间：
2016-11-01
影响因子：
6.4
通讯作者：
Denny, Joshua C.
中科院分区：
文献类型：
--
作者：
Kirby, Jacqueline C.;Speltz, Peter;Denny, Joshua C.
目的医疗保健数据已成为临床和基因组研究的重要来源。通常，研究人员创建并迭代改进表型算法以实现高阳性预测值（PPV）或灵敏度，从而识别有效病例和对照。这些算法实现了最大的效用时，验证和共享的多个卫生保健systems.Materials和方法，我们报告的表型知识库（PheKB，http://phekb.org），支持工作流程的建设，共享和验证电子表型算法的在线环境的当前状态和影响。我们分析了算法中使用最频繁的组件及其在创作机构和二级实现sites.Results中的性能，截至2015年6月，PheKB包含30个最终的表型算法和62个正在开发的算法，涵盖了一系列的性状和疾病。表型在6个月内有超过3500个独特的观点，并已被其他机构重新使用。国际疾病分类代码是最常用的组成部分，其次是药物和自然语言处理。在已发表性能数据的算法中，在编写机构进行评估时，PPV的中位数几乎相同（n = 44;病例组96.0%，对照组100%）与实施研究中心相比（n = 40;病例97.5%，控制100%）讨论这些结果表明，可以开发广泛的算法来挖掘来自不同健康系统的电子健康记录数据结论通过提供一个中央存储库，PheKB能够使用医疗保健生成的数据改进研究级表型算法的开发、可移植性和有效性。
Objective Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems.Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites.Results As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%).Discussion These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others.Conclusion By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.