Personalized prediction of initial valproic acid dose in children with epilepsy using machine learning techniques

The TabNet-based model demonstrated strong predictive ability for estimating individualized initial VPA doses in pediatric epilepsy in a single-center cohort with internal validation only. By integrating readily available demographic and laboratory parameters, the model and its accompanying online tool have the potential to offer a practical decision-support resource for clinicians and pharmacists to improve dosing precision, enhance safety, and optimize therapeutic outcomes; however, the generalizability of this internally validated model to other hospitals, populations, and ethnic groups is unknown, and external validation is required before clinical implementation.