Kerley Cailey I, Nguyen Tin Q, Ramadass Karthik, Cutting Laurie E, Landman Bennett A, Berger Matthew
Department of Electrical & Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA.
Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
JAMIA Open. 2023 Apr 3;6(1):ooad018. doi: 10.1093/jamiaopen/ooad018. eCollection 2023 Apr.
OBJECTIVE: To enable interactive visualization of phenome-wide association studies (PheWAS) on electronic health records (EHR). MATERIALS AND METHODS: Current PheWAS technologies require familiarity with command-line interfaces and lack end-to-end data visualizations. pyPheWAS Explorer allows users to examine group variables, test assumptions, design PheWAS models, and evaluate results in a streamlined graphical interface. RESULTS: A cohort of attention deficit hyperactivity disorder (ADHD) subjects and matched non-ADHD controls is examined. pyPheWAS Explorer is used to build a PheWAS model including sex and deprivation index as covariates, and the Explorer's result visualization for this model reveals known ADHD comorbidities. DISCUSSION: pyPheWAS Explorer may be used to rapidly investigate potentially novel EHR associations. Broader applications include deployment for clinical experts and preliminary exploration tools for institutional EHR repositories. CONCLUSION: pyPheWAS Explorer provides a seamless graphical interface for designing, executing, and analyzing PheWAS experiments, emphasizing exploratory analysis of regression types and covariate selection.
目的:实现对电子健康记录(EHR)上全表型组关联研究(PheWAS)的交互式可视化。 材料与方法:当前的PheWAS技术需要熟悉命令行界面,且缺乏端到端的数据可视化。pyPheWAS Explorer允许用户在简化的图形界面中检查分组变量、检验假设、设计PheWAS模型并评估结果。 结果:对一组注意力缺陷多动障碍(ADHD)受试者和匹配的非ADHD对照进行了研究。使用pyPheWAS Explorer构建了一个PheWAS模型,将性别和贫困指数作为协变量,该模型在Explorer中的结果可视化显示了已知的ADHD合并症。 讨论:pyPheWAS Explorer可用于快速研究潜在的新型EHR关联。更广泛的应用包括供临床专家使用以及作为机构EHR存储库的初步探索工具。 结论:pyPheWAS Explorer为设计、执行和分析PheWAS实验提供了一个无缝的图形界面,强调对回归类型和协变量选择的探索性分析。
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