Sepsis refers to a systemic inflammatory response resulting from infections， and it carries a high risk of mortality in intensive care settings. Existing predictive models often rely on extracting single feature subsets from a larger set， failing to fully utilize the complex interactions between feature subsets， known as structural mutual information. This limitation reduces prediction accuracy. Structural mutual information not only captures dependencies between features at the same level of granularity but also reveals complex relationships across different granularities， enabling more precise detection of subtle changes in a patient’s condition. To address this limitation， this study presents a novel sepsis prognosis model that deeply explores the structural mutual information within electronic health records， significantly enhancing the accuracy of mortality risk predictions. Experimental results show that the proposed model achieves notable improvements in predictive accuracy， providing clinicians with more dependable mortality risk assessments and clearer decision-making support.