Employment of Integrated Framework of Digital Twin, Quantum Computing, and Deep Learning for Healthcare and Iot Systems
Abstract
The Healthcare Internet of Things (H-IoT) enables patients to be constantly monitored through wearable and implantable devices; however, the current cloud-based and traditional deep learning methods have high latency, scalability issues, and decreased predictive accuracy. To overcome these issues, this paper presents an integrated framework that combines Digital Twin (DT), Deep Learning (DL), and Quantum Computing (QC) for intelligent and personalized Healthcare IoT. In the proposed method, Digital Twins for patient-specific data retain real-time estimation of the physiological state, and disease prediction and anomaly detection are carried out by deep learning models using multimodal sensor data. Hybrid quantum-classical models are also used to improve feature representation and optimization efficiency for high-dimensional healthcare data. The proposed framework is tested on benchmark healthcare datasets such as MIMIC-IV and PhysioNet, as well as simulated Digital Twin settings. The results show improvements of up to 10% in AUROC, 20% reduction in inference latency, and better robustness to noisy and missing data settings compared to traditional DT-DL frameworks. The experiments are performed on benchmark datasets and simulated Digital Twin settings, and real-world clinical applications are considered in future work
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