This monograph focuses on the system architecture and key technologies of the smart healthcare data engine, delving deeply into multiple dimensions such as data collection and governance strategies, privacy protection and compliance mechanisms, cross-modal data fusion algorithms, intelligent analysis models, cloud platforms and edge computing support, and intelligent operation and maintenance systems. Especially in terms of privacy protection, by integrating federated learning and differential privacy technologies, it has achieved support for multi-institution collaborative training and model sharing while ensuring the security of patients' sensitive information. At the crossmodal fusion level, the system presents a joint representation learning method based on deep neural networks, effectively promoting the complementarity and collaborative gain of multi-source medical data such as images, texts, and signals. In terms of intelligent algorithm integration, personalized prediction and intervention models for complex clinical scenarios such as chronic disease management, mental health, and neurorehabilitation have been proposed. By combining real-time data streams and historical information, precise health risk assessment and intervention plan recommendation are achieved.

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