Machine learning to predict antimicrobial resistance: future applications in clinical practice?

This review explores the use of machine learning (ML) in predicting antimicrobial resistance (AMR). The review included 36 studies, primarily based on hospital data and outpatient data, with the majority conducted in high-resource settings. The studies focused on predicting drug resistance in infected patients, ML-assisted antibiotic prescription, colonization with carbapenem-resistant bacteria, and national and international AMR trends. The most common inputs were demographic characteristics, previous antibiotic susceptibility testing, and prior antibiotic exposure. 92% of the studies targeted Gram-negative bacteria resistance prediction. The review concludes that ML can potentially aid in AMR prediction, but future research is needed to design, implement, and evaluate the effectiveness of ML decision support systems.