Bayesian approaches to clinical analyses for the purposes of patient phenotyping have been limited by the computational challenges associated with applying the Markov-Chain Monte-Carlo (MCMC) approach to large real-world data. Approximate Bayesian inference via optimization of the variational evidence lower bound, often called Variational Bayes (VB), has been successfully demonstrated for other applications. We investigate the performance and characteristics of currently available R and Python VB software for variational Bayesian Latent Class Analysis (LCA) of realistically large real-world observational data. We used a real-world data set, Optum\textsuperscript{TM} electronic health records (EHR), containing pediatric patients with risk indicators for type 2 diabetes mellitus that is a rare form in pediatric patients. The aim of this work is to validate a Bayesian patient phenotyping model for generality and extensibility and crucially that it can be applied to a realistically large real-world clinical data set. We find currently available automatic VB methods are very sensitive to initial starting conditions, model definition, algorithm hyperparameters and choice of gradient optimiser. The Bayesian LCA model was challenging to implement using VB but we achieved reasonable results with very good computational performance compared to MCMC.
Variational Bayes latent class approach for EHR-based phenotyping with large real-world data
University College Dublin（都柏林大学学院）；The Insight Centre for Data Analytics, University College Dublin（洞察数据分析中心，都柏林大学学院）；School of Mathematics & Statistics, University College Dublin（数学与统计学院，都柏林大学学院）；School of Medicine, University College Dublin（医学院，都柏林大学学院）
Bayesian Profile Regression using Variational Inference to Identify Clusters of Multiple Long-Term Conditions Conditioning on Mortality in Population-Scale Data
Health Data Research UK, Swansea University Medical School, Swansea University（英国健康数据研究中心，斯旺西大学医学院，斯旺西大学）；Department of Statistics, University of Warwick（华威大学统计系）；Department of Pharmacology and Therapeutics, University of Liverpool（利物浦大学药理学与治疗学系）；Division of Cancer and Genetics, Cardiff University（卡迪夫大学癌症与遗传学部）
