Antimicrobial Resistance (AMR) poses a significant global public health challenge, necessitating early detection strategies to enable timely clinical interventions. Electronic Health Records (EHRs) offer extensive real-world clinical data but present challenges due to their irregularly sampled, heterogeneous, and multivariate temporal structure. This paper investigates graph-based learning models to predict AMR in Intensive Care Unit patients by systematically modeling spatial and temporal dependencies within EHR data represented as Multivariate Time Series. We propose and evaluate a novel Spatio-Temporal Graph Convolutional Neural Network architecture, demonstrating its superior predictive performance by achieving a Receiver Operating Characteristic Area Under the Curve of 80.00%, surpassing baseline models by approximately 6%. Furthermore, our analysis of the learned graph structures highlights critical clinical interactions, notably emphasizing catheter-related variables as central nodes, aligning well with established clinical knowledge. By combining high predictive performance with enhanced interpretability, our approach presents a robust and transparent framework, well-suited for clinical applications aimed at improving AMR risk assessment and patient care management.