安全的雾计算用于远程健康监测,数据优先级和基于人工智能的异常检测
Kiran Fahd1, Sazia Parvin1, Antony Di Serio1
1Department of Business and Construction, Melbourne Polytechnic, Preston, VIC 3072, Australia.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种基于雾的新型框架,用于智能远程健康监测,增强安全性和实时数据分析,以提供个性化的患者护理. 该系统使用人工智能对关键健康数据进行优先排序,以提高早期干预的准确性.
科学领域:
- 网络物理系统 网络物理系统
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 医疗保健技术 医疗保健技术 医疗保健技术
背景情况:
- 云架构为智能健康监控带来延迟,带宽和隐私问题.
- 雾架构提供了接近性,但面临着敏感患者数据的数据管理,安全和隐私挑战.
研究的目的:
- 解决当前远程健康监测系统的局限性.
- 提出一个创新的基于雾的框架,整合安全通信和智能数据优先级 (IDP).
- 为了增强异常和威胁检测实时患者护理.
主要方法:
- 开发了一个基于雾的框架,具有安全的通信和IDP.
- 集成基于人工智能的增强随机森林模型,用于异常和威胁检测.
- 利用模拟智能医疗保健场景与可穿戴设备的合成数据.
主要成果:
- 使用人工智能和基于规则的值,动态优先考虑的健康特征 (心率,SpO2,呼吸率).
- 实现了高预测性能:93.5%的准确性,90.8%的精度,88.7%的回忆和89.7%的F1分数.
- 证明了对关键健康异常的实时安全传输的成功概念验证.
结论:
- 拟议的基于雾的框架有效地增强了安全的远程健康监测.
- 智能数据优先级和人工智能驱动的异常检测提高了实时干预能力.
- 该框架支持临床医生提供及时,准确的数据,以提供个性化的患者护理.
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