Advanced Graph-Based Approaches for Predicting Antimicrobial Resistance in Intensive Care Units

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 catheterrelated 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.