ABSTRACT
This study leverages machine learning algorithms including regression analysis, decision trees, and neural networks on a clinical dataset to improve the prediction of analgesic drug concentrations, aiming to optimize pain management and minimize side effects. The models, particularly neural networks, significantly outperformed traditional pharmacokinetic methods in metrics like MAE and RMSE by effectively capturing nonlinear dynamics. This demonstrates the potential for personalized, adaptive dosing strategies that can incorporate real-time patient data. Future research will expand datasets to include diverse populations and genomic factors and explore reinforcement learning for dynamic dosing, highlighting the transformative role of machine learning in advancing personalized analgesic therapy.
