Artificial intelligence and precision medicine: a pilot study predicting optimal ceftaroline dosage for pediatric patients.

ABSTRACT:

Background

Accurate drug dosing in pediatrics is complicated by age-related physiological variability. Standard weight-based dosing may result in either subtherapeutic exposure or toxicity. Machine learning (ML) models can capture complex relationships among clinical variables and support individualized therapy.

Methods

We analyzed clinical and pharmacokinetic data from 20 pediatric patients enrolled in the PUERI study (January 2020-November 2021, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy). Eight ML models-including linear regression (LR), ridge regression (RR), lasso regression (LaR), Huber regression (HR), random forest (RF), XGBoost, LightGBM, and a neural network (MLP)-were trained to predict ceftaroline doses that would achieve plasma concentrations clos

SUBMITTER: Frasca M 

PROVIDER: S-EPMC12856755 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature