A data-driven AI framework for personalized diagnosis, prognosis, and therapeutic optimization in chronic disease management using multimodal big data analytics

Experimental evaluations on large-scale multimodal datasets demonstrate superior performance in outcome prediction accuracy, intervention personalization, and trajectory alignment, underscoring the practical applicability of the system in chronic care settings. By combining patient-specific temporal modeling with adaptive therapeutic recalibration, this framework represents a significant advancement toward scalable, intelligent, and individualized chronic disease management leveraging AI and big data infrastructures.