A machine learning system that predicts antibiotic resistance using only organism and antibiotic identifiers — the minimal data available in any basic lab report. Bridges the gap in resource-limited clinical settings where genomic sequencing is unavailable.

Full system design and evaluation of AMR-X — an end-to-end ML platform for antimicrobial resistance prediction. Evaluated across clinical datasets with XGBoost achieving state-of-the-art performance on sparse clinical identifiers.