Supervised Machine Learning to Identify Hospital Inpatients Needing a Change of Antibiotic Therapy in Real Time: Preclinical Diagnostic Evaluation and Feasibility Study.

ABSTRACT:

Background

Postprescription review (PPR) by clinical microbiology/infectious diseases specialists is a proven intervention for optimizing antibiotic management in hospitals. However, hospitals lack sufficient staff to conduct PPR at scale. This study investigated the feasibility and diagnostic performance of supervised machine learning to monitor electronic medical records and prioritize PPR in real time.

Methods

Over 2 years, infection specialists categorized PPR recommendations as recommendations to "stop," "change," or "continue" therapy at an acute hospital in London, United Kingdom. These labels (n = 2625) were linked to features generated from electronic patient records. Random forest, XGBoost, and C5 classifiers were trained before undergoing prospective evaluation in

SUBMITTER: Dutey-Magni PF 

PROVIDER: S-EPMC12817996 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature