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  "Title": "Variational Bayes for Latent Patient Phenotypes in EHR",
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  "Authors@R": "c(\nperson(\"Brian\", \"Buckley\", , \"brian.buckley.1@ucdconnect.ie\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-5478-5830\")),\nperson(\"Adrian\", \"O'Hagan\", , \"adrian.ohagan@ucd.ie\", role = c(\"aut\"),\ncomment = \"Co-author\"),\nperson(\"Marie\", \"Galligan\", , \"marie.galligan@ucd.ie\", role = c(\"aut\"),\ncomment = \"Co-author\")\n)",
  "Description": "Identification of Latent Patient Phenotype from Electronic\nHealth Records (EHR) Data using Variational Bayes Gaussian\nMixture Model for Latent Class Analysis and Variational Bayes\nregression for Biomarker level shifts, both implemented by\nCoordinate Ascent Variational Inference algorithms. Variational\nmethods are used to enable Bayesian analysis of very large\nElectronic Health Records data. For VB GMM details see Bishop\n(2006,ISBN:9780-387-31073-2). For Logistic VB see Jaakkola and\nJordan (2000) <doi:10.1023/A:1008932416310>. Please see\npreprint of JSS-submitted paper\n<doi:10.48550/arXiv.2512.14272>.",
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