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Rapid and accurate diagnostics of bacterial infections are necessary for efficient treatment of antibiotic-resistant pathogens. Cultivation-based methods, such as antibiotic susceptibility testing (AST), are slow, resource-demanding, and can fail to produce results before the treatment needs to start. This increases patient risks and antibiotic overprescription. Here, we present a deep-learning method that uses transformers to merge patient data with available AST results to predict antibiotic susceptibilities that have not been measured. The method is combined with conformal prediction (CP) to enable the estimation of uncertainty at the patient-level. After training on three million AST results from thirty European countries, the method made accurate predictions for most antibiotics while controlling the error rates, even when limited diagnostic information was available. We conclude that transformers and CP enables confidence-based decision support for bacterial infections and, thereby, offer new means to meet the growing burden of antibiotic resistance.