Personalized Antibiograms: Machine Learning for Precision Selection of Empiric Antibiotics.

Up to 50% of antibiotic use in hospital settings is suboptimal. We build machine learning models trained on electronic health record data to minimize wasteful use of antibiotics. Our classifiers flag no growth blood and urine microbial cultures with high precision. Further, we build models that predict the likelihood of bacterial susceptibility to sets of antibiotics. These models contain decision thresholds that separate subgroups of patients whose susceptibility rates to narrow-spectrum antibiotics equal overall susceptibility rates to broader-spectrum drugs. Retroactively analyzing these thresholds on our one year test set, we find that 14% of patients infected with Escherichia coli and empirically treated with piperacillin/tazobactam could have been treated with ceftriaxone with coverage equal to the overall susceptibility rate ofpiperacillin/tazobactam. Similarly, 13% of the same cohort could have been treated with cefazolin - a first generation cephalosporin.