Variational Bayes latent class analysis for EHR-based phenotyping with large real-world data

Variational Bayes latent class analysis for EHR-based phenotyping with large real-world data

IntroductionBayesian approaches to patient phenotyping in clinical observational studies have been limited by the computational challenges associated with applying the Markov Chain Monte Carlo (MCMC) approach to real-world data. Approximate Bayesian inference via optimization of the variational evid...

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Bibliografische gegevens
Hoofdauteurs: Brian Buckley, Adrian O'Hagan, Marie Galligan
Formaat: Artikel
Taal:English
Gepubliceerd in: Frontiers Media S.A. 2024-10-01
Reeks:Frontiers in Applied Mathematics and Statistics
Onderwerpen:
variational Bayes
latent class analysis
patient phenotyping
real-world evidence
electronic health records
Online toegang:https://www.frontiersin.org/articles/10.3389/fams.2024.1302825/full

Internet

https://www.frontiersin.org/articles/10.3389/fams.2024.1302825/full

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