A variational Bayes latent class approach for EHR-based patient phenotyping in R

Brian Buckley; Adrian O'Hagan; Marie Galligan

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arXiv · 2512.14272

A variational Bayes latent class approach for EHR-based patient phenotyping in R

Brian Buckley·Adrian O'Hagan·Marie Galligan

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Abstract

The VBphenoR package for R provides a closed-form variational Bayes approach to patient phenotyping using Electronic Health Records (EHR) data. We implement a variational Bayes Gaussian Mixture Model (GMM) algorithm using closed-form coordinate ascent variational inference (CAVI) to determine the patient phenotype latent class. We then implement a variational Bayes logistic regression, where we determine the probability of the phenotype in the supplied EHR cohort, the shift in biomarkers for patients with the phenotype of interest versus a healthy population and evaluate predictive performance of binary indicator clinical codes and medication codes. The logistic model likelihood applies the latent class from the GMM step to inform the conditional.

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Brian Buckley, Adrian O'Hagan, Marie Galligan. 2025-12-16. A variational Bayes latent class approach for EHR-based patient phenotyping in R. https://arxiv.org/abs/2512.14272

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