Application of Machine Learning Models to Predict Tigecycline Exposure in Critically Ill Patients Using Different Limited Sampling Strategies

Background

The high variability in the pharmacokinetics of tigecycline may impact therapeutic efficacy, particularly in critically ill patients. This study introduces machine learning (ML) methods, aiming to clarify their effectiveness in individualized drug exposure prediction and determine the optimal modeling strategy.