A Secure Digital Twin Healthcare Framework for Precision Medicine: Integrating IoMT, ML and Fog Computing

Digital Twin is a virtual replica of a physical system that uses real-time sensor data along with historical data to predict and simulate behaviors. In healthcare, the Digital Patient Twin represents a complete virtual copy of an individual’s medical records updated in real-time with data from tests and sensors embedded in wearable devices. Integrated care offered by traditional healthcare systems encounters a challenge in providing real-time monitoring due to the lack of interoperability between IoT devices and electronic health record (EHR) systems. Machine learning (ML)-based predictive modeling facilitates timely interventions and personalized treatment strategies, which have been shown to improve patient outcomes. Fog computing enhances the speed of data processing, which is based on IoT, but also poses many security issues in the following ways: This project investigates how Digital Twin technology can solve these challenges through the power of real-time monitoring, providing personalized care while enhancing IoT-connected healthcare security. Along with this, it also works on advanced predictive time series analytics, virtual scenarios for the optimization of IoT sensor networks, securing patient data against threats, etc. The project also integrates the use of cloud and fog infrastructures to facilitate scalable data storage, high availability, and rapid processing of data for real-time insights and decision-making.