Machine Learning-Based Prediction of Individualized Drug Dosing in Pediatric Patients Using Clinical and Demographic Parameters

Abstract

Pediatric drug dosing remains a significant clinical challenge because children exhibit substantial variability in drug absorption, distribution, metabolism, and excretion throughout growth and development. Conventional dosing approaches, which primarily rely on body weight, body surface area, or age-based dosing guidelines, may not adequately account for individual differences in physiological development, organ function, disease severity, concomitant medications, and genetic factors. Machine learning (ML) offers an opportunity to improve individualized drug dosing by integrating clinical, demographic, laboratory, and pharmacokinetic parameters to estimate appropriate dose requirements and predict the likelihood of adverse drug reactions or subtherapeutic exposure.

This review-oriented research paper examines the potential application of ML techniques, including random forest, gradient boosting, support vector regression, artificial neural networks, and ensemble learning, in pediatric dose prediction. Relevant input variables include age, body weight, height, body surface area, gestational age, renal and hepatic function, serum drug concentrations, clinical diagnosis, and concomitant medication use. The manuscript outlines a conceptual framework for collecting clinical data, preprocessing variables, training predictive models, evaluating performance, and integrating model recommendations into clinical decision-making. Particular attention is given to therapeutic drug monitoring, model interpretability, data privacy, population representation, and the distinctive ethical and safety requirements of pediatric research.

The proposed framework emphasizes that machine learning should support, rather than replace, clinical judgment and established dosing protocols. Before implementation, predictive models require rigorous external validation, prospective evaluation, and assessment of clinical utility across diverse pediatric populations. When appropriately developed and validated, ML-based approaches may contribute to safer, more precise, and more individualized pharmacotherapy for children.