Machine-learning-driven predictions for antimicrobial resistance could play a role in addressing looming global health crisis

Summary

MBZUAI researchers developed a machine-learning method to predict antimicrobial resistance (AMR) by analyzing electronic health records. The system predicts if a patient will experience AMR when prescribed an antibiotic or if infected with a bacterium. Published in Scientific Reports, the innovation helps physicians identify patients at risk for AMR by using patient demographics, lab results, and physician notes. Why it matters: This approach can help combat the rise of drug-resistant bacteria by providing timely predictions and supporting more informed prescription decisions.