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Found 4 papers, 3 papers with code
Scalable Bayesian inference for the generalized linear mixed model
The generalized linear mixed model (GLMM) is a popular statistical approach for handling correlated data, and is used extensively in applications areas where big data is common, including biomedical data settings.
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma
As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well.
A spatially varying change points model for monitoring glaucoma progression using visual field data
Glaucoma disease progression, as measured by visual field (VF) data, is often defined by periods of relative stability followed by an abrupt decrease in visual ability at some point in time.
Applications
Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method
We introduce a spatiotemporal boundary detection model that allows the underlying anatomy of the optic disc to dictate the spatial structure of the VF data across time.
Applications
