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

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 evaluation o...

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Detalles Bibliográficos
Autores principales: Bologa, Cristian G., Pankratz, Vernon Shane, Unruh, Mark L., Roumelioti, Maria Eleni, Shah, Vallabh, Shaffi, Saeed Kamran, Arzhan, Soraya, Cook, John, Argyropoulos, Christos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Technical Advance
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8310602/
https://www.ncbi.nlm.nih.gov/pubmed/34303362
http://dx.doi.org/10.1186/s12874-021-01318-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8310602/
https://www.ncbi.nlm.nih.gov/pubmed/34303362
http://dx.doi.org/10.1186/s12874-021-01318-6

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