Antimicrobial resistance is recognized by the World Health Organization as a significant global health threat. The accurate identification of bacterial susceptibility to antibiotics is crucial. However, clinical laboratories often take several days to complete this process, and practitioners rely on probabilistic and empirical reasoning, coupled with local hospital guidelines. In this work, we propose an attention-based bidirectional-Long Short-Term Memory network to predict antibiotic resistance. More precisely, the model is able to give predictions at each stage of the bacterial identification process for a set of 47 antibiotics and combination, in order to support clinicians in their decision process. Excellent results were achieved, with the area under the receiver operating characteristic curve and area under the precision-recall curve reaching up to 0.9, averaged across all antibiotics, at the last stages. The attention mechanism was used to visualize the importance attributed to each features, allowing better interpretation. Additionally, the model is integrated into a user-friendly and responsive web application, accessible on both mobile phones and desktops, to be used as a clinical a decision support system.
