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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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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.

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|December 11, 2025
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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.

关键词:
基于人工智能的异常检测检测.边缘计算是一种边缘计算.雾计算 雾计算 雾计算智能数据优先排序智能数据优先排序智能远程医疗监控 智能远程医疗监控

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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.