Predicting future hospital antimicrobial resistance prevalence using machine learning

Abstract Objectives Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at a hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Methods Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April-March) for 22 pathogen-antibiotic combinations (FY2016-2017-FY2021-2022). XGBoost model predictions were compared in a to previous value taken forwards, difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were to aid interpretability.