The objective of this research is to investigate the foundations, methodologies, algorithms and implementations of cyberphysical networks in the context of medical applications. The approach is to design, implement and study Carenet, a medical care network, by investigating three critical issues in the design and construction of cyberphysical networks: (1) rare event detection and multidimensional analysis in cyberphysical data streams, (2) reliable and trusted data analysis with cyberphysical networks, including veracity analysis for object consolidation and redundancy elimination, entity resolution and information integration, and feedback interaction between cyber- and physical- networks, and (3) spatiotemporal data analysis including spatiotemporal cluster analysis, sequential pattern mining, and evolution of cyberphysical networks. Intellectual merit: This project focuses on several most pressing issues in large-scale cyberphysical networks, and develops foundations, principles, methods, and technologies of cyberphysical networks. It will deepen our understanding of the foundations, develop effective and scalable methods for mining such networks, enrich our understanding of cyberphysical systems, and benefit many mission-critical applications. The study will enrich the principles and technologies of both cyberphysical systems and information network mining. Broader impacts: The project will integrate multiple disciplines, including networked cyberphysical systems, data mining, and information network technology, and advance these frontiers. It will turn raw data into useful knowledge and facilitate strategically important applications, including the analysis of patient networks, combat networks, and traffic networks. Moreover, the project systematically generates new knowledge and contains a comprehensive education and training plan to promote diversity, publicity, and outreach.