Personalized antibiograms for machine learning driven antibiotic selection.

ABSTRACT:

Background

The Centers for Disease Control and Prevention identify antibiotic prescribing stewardship as the most important action to combat increasing antibiotic resistance. Clinicians balance broad empiric antibiotic coverage vs. precision coverage targeting only the most likely pathogens. We investigate the utility of machine learning-based clinical decision support for antibiotic prescribing stewardship.

Methods

In this retrospective multi-site study, we developed machine learning models that predict antibiotic susceptibility patterns (personalized antibiograms) using electronic health record data of 8342 infections from Stanford emergency departments and 15,806 uncomplicated urinary tract infections from Massachusetts General Hospital and Brigham & Women's Hospital in B

SUBMITTER: Corbin CK 

PROVIDER: S-EPMC9053259 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature