XAI-Driven Multimodal Deep Learning for Early Sepsis Prediction in ICU

Abstract

Early detection of sepsis in intensive care units (ICUs) remains a critical challenge due to the rapid progression of the condition and the complexity of physiological signals associated with its onset. Advances in artificial intelligence, particularly deep learning, have enabled the development of predictive models capable of identifying early warning signs of sepsis from large-scale clinical datasets. However, many of these models operate as black-box systems, limiting their interpretability and reducing clinical trust. This study presents an explainable artificial intelligence (XAI)-driven multimodal deep learning framework designed to improve early sepsis prediction in ICU environments. The proposed approach integrates multiple healthcare data modalities, including vital signs, laboratory measurements, and electronic health records, to capture complex interactions among clinical variables. In addition to achieving high predictive performance, the framework incorporates explainability techniques that highlight the most influential clinical features contributing to the model’s predictions. The results demonstrate that the multimodal model improves prediction accuracy and enables earlier detection of sepsis compared to traditional machine learning approaches, while also providing transparent insights to support clinical decision-making. The findings highlight the potential of combining multimodal deep learning and explainable AI to enhance patient monitoring systems and assist healthcare professionals in making timely and informed interventions in critical care settings.