标题： 基于5G网络的实时健康监测：一种用于远程患者护理的深度学习架构 显示英文标题

标题： RealTime Health Monitoring Using 5G Networks: A Deep Learning-Based Architecture for Remote Patient Care

摘要： 远程患者监测在现代医疗中至关重要，但当前系统在实时分析和预测生命体征方面面临挑战。 本文提出了一种结合深度学习与5G网络能力的新架构，以实现生命体征的实时监测和预测。 所提出的系统利用了针对边缘部署优化的混合CNN-LSTM模型，并结合5G超可靠低延迟通信（URLLC）以实现高效的数据传输。 该架构实现了端到端延迟14.4ms，同时在多个生命体征上保持96.5%的预测准确率。 我们的系统相比现有解决方案有显著改进，与当前最先进的系统相比，延迟降低了47%，预测准确率提高了4.2%。 在三个月内对1000名患者的数据显示，性能评估验证了该系统在临床环境中的可靠性和可扩展性。 结果表明，将深度学习与5G技术相结合可以有效解决实时患者监测的挑战，从而实现病情恶化的早期检测和改善临床结果。 这项研究建立了一个可靠的、实时的生命体征监测和预测框架，用于数字医疗。 显示英文摘要

摘要： Remote patient monitoring is crucial in modern healthcare, but current systems struggle with real-time analysis and prediction of vital signs. This paper presents a novel architecture combining deep learning with 5G network capabilities to enable real-time vital sign monitoring and prediction. The proposed system utilizes a hybrid CNN-LSTM model optimized for edge deployment, paired with 5G Ultra-Reliable Low-Latency Communication (URLLC) for efficient data transmission. The architecture achieves end-to-end latency of 14.4ms while maintaining 96.5% prediction accuracy across multiple vital signs. Our system shows significant improvements over existing solutions, reducing latency by 47% and increasing prediction accuracy by 4.2% compared to current state-of-the-art systems. Performance evaluations conducted over three months with data from 1000 patients validate the system's reliability and scalability in clinical settings. The results demonstrate that integrating deep learning with 5G technology can effectively address the challenges of real-time patient monitoring, leading to early detection of deteriorating conditions and improved clinical outcomes. This research establishes a framework for reliable, real-time vital sign monitoring and prediction in digital healthcare.