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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...
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 |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
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 |
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