A Unified Namespace–Driven Digital Twin Framework for Real Time Control of Multiscale Biomedical Systems

Digital twins are becoming an important tool in biomedical systems. They help with real time monitoring, prediction, and control. They work well only when they can combine many types of physiological data. They must also stay closely in sync with the real system.
This paper describes a digital twin framework that uses a Unified Namespace. The UNS acts as a central data hub. It collects signals from sensors, organ level models, and patient information. It keeps all data in one clear and interoperable structure. It separates data producers from data users. This makes the system easier to scale. It also supports fast data flow and constant model updates.
A multiscale computational model sits at the center of the twin. It joins physiological behavior with predictive methods. It supports real time decisions in a closed loop system. A sample biomedical case shows how the UNS improves system speed, prediction quality, and control actions. The results show that UNS based digital twins can support personalized medicine. They can also improve biomedical workflows and help build advanced cyber physical healthcare systems.