Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic–Machine Learning Approach

We evaluated four machine learning algorithms, including LASSO, random forest (RF), K-nearest neighbor (KNN), and gradient boosted machine (GBM). The ensemble models (GBM and RF) achieved superior performance, with internal validation results indicating no overfitting. The root mean square errors for RF and GBM were 0.183 mg h/L and 0.185 mg h/L respectively, while mean absolute errors of RF and GBM were 0.103 mg h/L and 0.102 mg h/L, respectively CONCLUSIONS: We demonstrate the feasibility of developing population pharmacokinetic-informed machine learning models to accurately predict early ketamine exposures in pediatric status, where pharmacokinetic sampling is a challenge.