Digital Twin-Based Intelligent Healthcare System for Predictive Patient Monitoring

Abstract

The integration of advanced technologies in healthcare has led to the emergence of digital twins for personalized and predictive patient monitoring. This paper presents a Digital Twin-Based Intelligent Healthcare System designed to create virtual replicas of patients using real-time physiological and clinical data. The proposed framework continuously synchronizes patient data from wearable devices, electronic health records, and clinical inputs to build dynamic digital models. Machine learning and deep learning algorithms are applied to analyze patient-specific patterns and predict potential health risks before they become critical. The system enables simulation of treatment strategies and supports personalized clinical decision-making. Additionally, edge and cloud computing technologies are integrated to ensure real-time processing, scalability, and efficient data management. The framework incorporates security mechanisms to protect sensitive patient information. Experimental results demonstrate that the proposed approach improves prediction accuracy, enables early intervention, and enhances overall healthcare efficiency. Integration with prior research in healthcare analytics strengthens system robustness and adaptability. The study highlights the potential of digital twin technology in transforming healthcare into a proactive, personalized, and data-driven ecosystem.