High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets

High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets

Abstract Background Converting electronic health record (EHR) entries to useful clinical inferences requires one to address the poor scalability of existing implementations of Generalized Linear Mixed Models (GLMM) for repeated measures. The major computational bottleneck concerns the numerical eval...

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Bibliographic Details
Main Authors: Cristian G. Bologa, Vernon Shane Pankratz, Mark L. Unruh, Maria Eleni Roumelioti, Vallabh Shah, Saeed Kamran Shaffi, Soraya Arzhan, John Cook, Christos Argyropoulos
Format: Article
Language:English
Published: BMC 2021-07-01
Series:BMC Medical Research Methodology
Subjects:
Generalized linear mixed models
Laplace approximation
Adaptive Gaussian Hermite quadrature
Electronic health records
Dyskalemias
Markov chain Monte Carlo
Online Access:https://doi.org/10.1186/s12874-021-01318-6

Internet

https://doi.org/10.1186/s12874-021-01318-6

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