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pyPheWAS浏览器:一种用于表型-疾病关联探索性分析的可视化工具。

pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations.

作者信息

Kerley Cailey I, Nguyen Tin Q, Ramadass Karthik, Cutting Laurie E, Landman Bennett A, Berger Matthew

机构信息

Department of Electrical & Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA.

Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

出版信息

JAMIA Open. 2023 Apr 3;6(1):ooad018. doi: 10.1093/jamiaopen/ooad018. eCollection 2023 Apr.


DOI:10.1093/jamiaopen/ooad018
PMID:37021295
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10070037/
Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a8e/10070037/bead8dff4f1d/ooad018f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a8e/10070037/06875cc00f3f/ooad018f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a8e/10070037/990e85e25c04/ooad018f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a8e/10070037/bead8dff4f1d/ooad018f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a8e/10070037/06875cc00f3f/ooad018f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a8e/10070037/990e85e25c04/ooad018f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a8e/10070037/bead8dff4f1d/ooad018f3.jpg

相似文献

[1]
pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations.

JAMIA Open. 2023-4-3

[2]
pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis.

Neuroinformatics. 2022-4

[3]
INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES.

Pac Symp Biocomput. 2016

[4]
PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.

J Am Med Inform Assoc. 2024-11-1

[5]
Current Scope and Challenges in Phenome-Wide Association Studies.

Curr Epidemiol Rep. 2017-12

[6]
PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis.

medRxiv. 2023-7-30

[7]
Phenome-Wide Association Studies Uncover a Novel Association of Increased Atrial Fibrillation in Male Patients With Systemic Lupus Erythematosus.

Arthritis Care Res (Hoboken). 2018-11

[8]
Phenome-Wide Association Study of Autoantibodies to Citrullinated and Noncitrullinated Epitopes in Rheumatoid Arthritis.

Arthritis Rheumatol. 2017-4

[9]
PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

Bioinformatics. 2021-7-19

[10]
R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment.

Bioinformatics. 2014-8-15

本文引用的文献

[1]
pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis.

Neuroinformatics. 2022-4

[2]
PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

Bioinformatics. 2021-7-19

[3]
Using phecode analysis to characterize co-occurring medical conditions in autism spectrum disorder.

Autism. 2021-4

[4]
Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation.

JMIR Med Inform. 2019-11-29

[5]
Material community deprivation and hospital utilization during the first year of life: an urban population-based cohort study.

Ann Epidemiol. 2018-11-29

[6]
RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis.

IEEE Trans Vis Comput Graph. 2018-9-13

[7]
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis.

IEEE J Biomed Health Inform. 2017-10-27

[8]
Secondary use of electronic medical records for clinical research: Challenges and Opportunities.

Converg Sci Phys Oncol. 2018-3

[9]
Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank.

Int J Epidemiol. 2018-2

[10]
Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.

PLoS One. 2017-7-7

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