BACKGROUND AND OBJECTIVE: Antimicrobial resistance is recognized by the World Health Organization as a significant global health threat. In clinical practice, the accurate identification of bacterial susceptibility to antibiotics is crucial. However, clinical laboratories often take several days to complete this process and, in the meantime, physicians rely on probabilistic and empirical reasoning, coupled with local hospital guidelines. METHODS: In this work, we propose an attention-based bidirectional-Long Short-Term Memory recurrent neural network as a clinical decision support system to predict antibiotic resistance at the patient's bedside prior to the arrival of final antimicrobial testing results from the laboratory. More precisely, the model gives predictions at each stage of the bacterial identification process for a set of 47 single antibiotics and combinations, to support clinicians in their prescribing decision. RESULTS: Great results were achieved, with a mean area under the receiver operating characteristic curve and a mean area under the precision-recall curve reaching up to 0.9. The attention mechanism was used to visualize the importance attributed to each feature and to better interpret the prediction results. CONCLUSION: The model has been integrated into a user-friendly and responsive web application, accessible on both mobile phones and desktops, to be used as a prototype clinical decision support system.