Real-Time Health Monitoring Using 5G Networks: Deep Learning-Based Architecture for Remote Patient Care.

Background: Remote patient monitoring systems face critical challenges in real-time vital sign analysis and secure data transmission. Objective: This study aimed to develop a novel architecture integrating deep learning with 5G networks for real-time vital sign monitoring and prediction. Methods: A hybrid convolutional neural network-long short-term memory model with attention mechanisms was optimized for edge deployment using 5G ultrareliable low-latency communication. The system incorporated end-to-end encryption and HIPAA (Health Insurance Portability and Accountability Act) compliance. Performance was evaluated over 3 months using data from 1000 patients. Results: The system demonstrated superior prediction accuracy and significantly reduced latency compared to existing solutions. Performance remained stable under adverse network conditions and across diverse patient populations, supporting thousands of concurrent monitoring sessions. Conclusions: This framework addresses security, scalability, and robustness requirements for clinical implementation, potentially improving patient outcomes through early detection of deteriorating conditions.