Harnessing the potential of multimodal EHR data: A comprehensive survey of clinical predictive modeling for intelligent healthcare

The digitization of healthcare has led to the accumulation of vast amounts of patient data through Electronic Health Records (EHRs) systems, creating significant opportunities for advancing intelligent healthcare. Recent breakthroughs in deep learning and information fusion techniques have enabled the seamless integration of diverse data sources, providing richer insights for clinical decision-making. This review offers a comprehensive analysis of predictive modeling approaches that leverage multimodal EHR data, focusing on the latest methodologies and their practical applications. We classify the current advancements from both task-driven and method-driven perspectives, while distilling key challenges and motivations that have fueled these innovations. This exploration examines the real-world impact of advanced technologies in healthcare, addressing issues from data integration to task formulation, challenges, and method refinement. The role of information fusion in enhancing model performance is also emphasized. Building on the discussions and findings, we highlight promising future research directions critical for advancing multimodal fusion technologies in clinical predictive modeling, addressing the complex challenges of real-world clinical environments, and moving toward universal intelligence in healthcare.