Abstract
Electronic Health Records (EHRs) serve as a comprehensive repository of multimodal patient health data, combining static demographic attributes with dynamic, irregular Multivariate Time Series (MTS), characterized by varying lengths. While MTS provide critical insights for clinical predictions, their integration with static features enables a more nuanced understanding of patient health trajectories and enhances predictive accuracy. Deep Neural Networks (DNNs) have proven highly effective in capturing complex patterns in healthcare data, offering a framework for multimodal data fusion. However, their adoption in clinical practice is limited by a lack of interpretability, as transparency and explainability are essential for supporting informed medical decisions. This study presents interpretable multimodal DNN architectures for predicting and understanding the emergence of Multidrug Resistance (MDR) in Intensive Care Units (ICUs). The proposed models integrate static demographic data with temporal variables, providing a holistic view of baseline patient characteristics and health progression. To address predictive performance and interpretability challenges, we introduce a novel methodology combining feature selection techniques with attention mechanisms and post-hoc explainability tools. This approach not only reduces feature redundancy but also highlights key risk factors, thereby improving model accuracy and robustness. Experimental results demonstrate the effectiveness of the proposed framework, achieving a Receiver Operating Characteristic Area Under the Curve of 76.90 ± 3.10, a significant improvement over baseline models. Beyond MDR prediction, this methodology offers a scalable and interpretable framework for addressing various clinical challenges involving EHR data. By integrating predictive accuracy with explanatory insights-such as the identification of key risk factors-this work supports timely, evidence-based interventions to improve patient outcomes in ICU settings.