Towards personalized guidelines: using machine-learning algorithms to guide antimicrobial selection

Abstract

Machine-learning algorithms have the potential to help clinicians predict antimicrobial resistance in patients found to have a Gram-negative infection of blood or urine. Prospective studies are required to assess performance in an unselected patient cohort, understand the acceptability of such systems to clinicians and patients, and assess the impact on patient outcome.