Vanderbilt Kennedy Center IDDRC Data Sciences Core (Core E) Lecture: Walkthrough of the PyPheWAS Toolkit
Thursday, Nov. 16, 12:00-1:00 p.m. CT
OMC 241 and Online (Microsoft Teams) -- Register using the links above
Presenter: Karthik Ramadass, Staff Engineer and Ph.D. Student, Landman Lab
In the era of digital healthcare, the explosion of electronic medical record (EMR) data presents a unique opportunity for researchers to uncover novel insights into disease associations. Phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) are powerful tools for this purpose but are often hampered by a lack of user-friendly software for large-scale EMR data analysis.
The PyPheWAS Toolkit addresses this gap by providing a straightforward command-line interface for researchers. It assists with a range of tasks, from data preparation to visualization of results. Specifically, pyPheWAS enables users to prepare data through steps like cohort censoring and age-matching. It supports traditional PheDAS analysis of ICD-9 and ICD-10 billing codes and can apply PheDAS analysis to a novel EMR phenotype mapping based on current procedural terminology (CPT) codes. Additionally, PyPheWAS can conduct novelty analysis of significant disease-phenotype associations discovered through PheDAS.