Variational Bayes for Latent Patient Phenotypes in EHR
Variational inference for Bayesian logistic regression using CAVI algo...
Run the Variational Bayes patient phenotyping model
Main algorithm function for the VB CAVI GMM
Calculate the Evidence Lower Bound (ELBO)
Variational Bayes Expectation step
Initialise the variational parameters and the hyper parameters
Variational Bayes Maximisation step
The VBphenoR package
Identification of Latent Patient Phenotype from Electronic Health Records (EHR) Data using Variational Bayes Gaussian Mixture Model for Latent Class Analysis and Variational Bayes regression for Biomarker level shifts, both implemented by Coordinate Ascent Variational Inference algorithms. Variational methods are used to enable Bayesian analysis of very large Electronic Health Records data. For VB GMM details see Bishop (2006,ISBN:9780-387-31073-2). For Logistic VB see Jaakkola and Jordan (2000) <doi:10.1023/A:1008932416310>. Please see preprint of JSS-submitted paper <doi:10.48550/arXiv.2512.14272>.
Useful links