EBGM V: development and evaluation of an artificial intelligence algorithm for child growth monitoring in primary care

This code repository is associated with the publication "Development, internal and external evaluation of an artificial intelligence algorithm for child growth monitoring in primary care". We provide the codes : to model individual height trajectories using the Jenss-Bayley non-linear mixed model according to sex to develop age-specific predictive models by age range and obtain thresholds for a pre-defined specificity. Fictive samples of the dataset are included in this repository as sample_G.rda (for boys) and sample_F (for girls). The full datasets are not publicly released due to privacy and licensing constraints. These samples include: id sex (1=boy, 2=girl) age (in days) height (in cm) x (outcome, 1=disease, 0=non-disease) ts (Turner syndrome, 1=oui, 0=non) ghd (Growth Hormone Deficiency, 1=oui, 0=non) referents (Non-disease, 1=oui, 0=non) controls (Modeling sample, 1=oui, 0=non)