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

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

doi:10.1093/jamiaopen/ooad018

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

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

Authors

Cailey I Kerley¹, Tin Q Nguyen^{2

3}, Karthik Ramadass⁴, Laurie E Cutting^{2

3}, Bennett A Landman^{1

2

4

5

6}, Matthew Berger⁴

Affiliations

¹ Department of Electrical & Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA.
² Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
³ Department of Special Education, Peabody College of Education and Human Development, Nashville, Tennessee, USA.
⁴ Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
⁵ Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.
⁶ Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.

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.

Keywords: ICD codes; PheWAS; electronic health records; interactive visualization.

Grants and funding

P50 HD103537/HD/NICHD NIH HHS/United States