Cloud-Integrated Digital Twins for Multisource Data Management in E-Health Care Systems Using Deep Transfer Learning

Abstract

In the modern era, healthcare systems are increasingly moving towards digitalization and integration with cloud technologies, driven by the need to manage and analyse vast amounts of patient data effectively. E-healthcare systems struggle to integrate multisource data from EHRs, wearables, sensors, and imaging due to format, quality, and frequency variations. IoT, Digital Twin (DT), and Deep Transfer Learning (DTL) further complicate data harmonization, synchronization, and predictive accuracy for effective patient health management. The objectives are to develop a unified framework for integrating multisource data from EHRs, wearables, sensors, and imaging, leveraging IoT, DT, and DTL to enhance data harmonization, synchronization, and predictive accuracy for improved patient health management. Cloud-Edge Collaborative Filtering (CECF) optimizes e-healthcare by preprocessing data at the edge, filtering in the cloud, and enhancing decision-making and privacy. Adaptive Federated Transfer Learning (AFTL) enables decentralized, personalized model training across devices, enhancing privacy, accuracy, and adaptation in e-healthcare. Hopfield Neural Networks (HNN) in cloud-based DTs enhance pattern recognition, anomaly detection, and real-time health monitoring in e-healthcare. Findings show that the system demonstrates high predictive accuracy 92%, efficient TL 85%, fast data processing 2 seconds, reduced operational costs of 20%, and improved patient outcomes, including a 95% satisfaction rate and 15% readmission rate and implemented in Python software. Future scope includes enhancing personalization with AI-driven models, improving real-time decision-making, expanding interoperability across systems, integrating genomic data, ensuring advanced security, and enabling global collaboration for optimized patient care.

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