Cailey I. Kerley, Tin Q. Nguyen, Karthik Ramadass, Laurie E. Cutting, Bennett A. Landman, and Matthew Berger. “pyPheWAS Explorer: A Visualization Tool for Exploratory Analysis of Phenome-Disease Associations.” JAMIA Open, vol. 6, no. 1, 2023,
Objective: This study aims to provide an easy-to-use tool for visualizing phenome-wide association studies (PheWAS) using electronic health records (EHR).
Materials and Methods: Current PheWAS tools are complicated, requiring command-line skills and lacking full visualizations. The new tool, pyPheWAS Explorer, offers a graphical interface to help users analyze variables, test assumptions, design models, and view results seamlessly.
Results: The tool was tested with data from individuals with attention deficit hyperactivity disorder (ADHD) and a control group. Using pyPheWAS Explorer, researchers created a model that included sex and socioeconomic status as factors. The tool effectively highlighted known ADHD-related health issues.
Discussion: pyPheWAS Explorer can quickly uncover new EHR associations, making it useful for clinical experts and as an initial exploration tool for institutional EHR databases.
Conclusion: pyPheWAS Explorer simplifies the process of designing, running, and analyzing PheWAS studies, focusing on exploratory data analysis and covariate selection through an intuitive graphical interface.
