A deep learning approach to predicting hospitalized patients’ SEIRD states using multimodal spatiotemporal data

Our findings underscore the efficacy of a hybrid deep learning strategy that skillfully combines specialized learning of spatial and temporal hospital data with a subsequent joint fine-tuning process. This innovative approach yields superior predictive capabilities for individual patient HAI risk, even when evaluated on a complex, real-world representative synthetic dataset. The demonstrated performance and methodological insights suggest significant potential for real-time clinical decision support and optimization of infection control measures. Its inherent adaptability makes it a promising foundation for deployment in diverse healthcare settings, with future work focused on validation with real-world clinical data.