Predicting future hospital antimicrobial resistance prevalence using machine learning.

ABSTRACT:

Background

Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate 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 to FY2021-2022). Extreme Gradient Boosting (XGBoost) model predictions were compared to the previous value taken forwards, the difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were calculated to aid interpretability.

Results

H

SUBMITTER: Vihta KD 

PROVIDER: S-EPMC11467333 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature