Implementation of a graph-embedded topic model for analysis of population-level electronic health records

To address the need for systematic investigation of the phenome enabled by ever-growing genotype and phenotype data, we describe our step-by-step software implementation of a graph-embedded topic model, including data preprocessing, graph learning, topic inference, and phenotype prediction. As a demonstration, we use simulated data that mimic the UK Biobank data as in our original study. We will demonstrate topic analysis to discover disease comorbidities and computational phenotyping via the inferred topic mixture for each subject. For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).(1)