Extended Data Fig. 9: Robustness of machine-learning-trained prescription models across age and gender and with respect to the clinical definition of resistance.

a, Frequency of mismatched treatment across all SDET cases, comparing physician’s prescriptions (dark bar) to algorithmic recommendations by the constrained and unconstrained models (cyan and magenta hatched, respectively) for females (top) and males (bottom) separated into three major age groups. b, Frequency of mismatched treatment across all SDET cases (Methods), when classifying ‘Intermediate’ level of resistance as ‘resistant’. Comparing mismatch frequencies of physicians’ prescriptions (dark bar) to algorithmic recommendations (light bars), either unconstrained (magenta hatched) or constrained for recommending drugs at the same ratio as physicians (cyan hatched). Also presented are the null expectations for randomly prescribing drugs with equal probabilities (random ‘dice’, magenta dashed) or for random drug permutations (random permutations, cyan dashed).