Extended Data Fig. 8: Schematic diagram of machine-learning-trained prescription models.

A set of samples with features of demographics, sample resistance history and antibiotic purchase history labeled for resistance to each antibiotic k (‘train set’) is used to train an antibiotic resistance prediction model (Methods: Logistic regression, terms 1–9). The model is applied to an SDET set of cases from the test period to calculate the probabilities of resistance to each antibiotic. In an unconstrained model the antibiotic with minimal probability for resistance is suggested. The calculated probabilities of resistance together with the respective prescriptions of the SDET set of cases are used to add a ‘cost’ term. In a constrained drug prescription model, the antibiotic with the minimal cost-adjusted probability is suggested.