Machine Learning in Antimicrobial Therapy for Critically Ill Patients: Optimizing Early Empirical Regimens, Individualized Dosing, and De-Escalation Strategies.

Abstract

The complexity of critically ill patients leads to high pharmacokinetic (PK) variability and an increased risk of drug resistance. For this special patient group, antimicrobial treatment regimens require individualized strategies, as traditional treatment models may have certain limitations in clinical practice. Machine learning (ML) has emerged as a novel tool for processing multidimensional clinical data. It could identify complex patterns to enhance diagnostic accuracy, treatment optimization, and drug behavior predictions across diverse populations. Based on the underlying power of ML, this review highlights its application in three critical domains: (1) predictive modeling of antimicrobial resistance (AMR) patterns to optimize the empirical antibiotic selection and mitigate resistance development; (2) data-driven forecasting of drug exposure to guide personalized dose adjustments; and (3) identify patients who potentially require antibiotic de-escalation therapy and optimize antimicrobial drug use while ensuring therapeutic efficacy. Furthermore, this paper suggests that ML algorithms could be combined with population pharmacokinetic (PopPK) models to construct an analytical framework with superior predictive performance and maintain interpretability. This method could provide a more accurate quantitative analysis of the dose-exposure-response relationship of antimicrobial drugs in critically ill patients. Despite these advances, challenges persist in data quality, clinical validation, and ethical regulation. Future research might prioritize prospective clinical trials to bridge the gap between theoretical models and bedside applications.