Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach.
Journal:
Journal of applied microbiology
Published Date:
Mar 1, 2020
Abstract
AIMS: Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjustment models are needed for benchmarking appropriate AU and for fair inter-facility comparison. In this study we identify patient- and facility-level predictors of antimicrobial usage in hospitalized patients using a machine learning approach, which can be used to inform a risk adjustment model to facilitate assessment of AU. To our knowledge, this is the first time machine learning has been applied for this purpose.