The spread of antimicrobial resistance (AMR) leads to challenging complications and losses of humanlives plus medical resources, with a high expectancy of deterioration in the future if the problem isnot controlled. From a machine learning perspective, data‑driven models could aid clinicians andmicrobiologists by anticipating the resistance beforehand. Our study serves as the first attempt toharness deep learning (DL) techniques and the multimodal data available in electronic health records(EHR) for predicting AMR. In this work, we utilize and preprocess the MIMIC‑IV database extensivelyto produce separate structured input sources for time‑invariant and time‑series data customized tothe AMR task. Then, a multimodality fusion approach merges the two modalities with clinical notes todetermine resistance based on an antibiotic or a pathogen. To efficiently predict AMR, our approachbuilds the foundation for deploying multimodal DL techniques in clinical practice, leveraging theexisting patient data.
Paper Link: https://rdcu.be/dN7vh