实时医院前紧急诊断的多源数据框架基于IoMT的远程医疗系统
Abdulrahman Ahmed Jasim1, Oguz Ata2, Omar Hussein Salman3
1Dept. of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey; Collage of Engineering, Al-Iraqia University, Baghdad, Iraq.
International journal of medical informatics
|September 2, 2024
概括
实时选优化框架 (RTOF) 使用医疗物联网 (IoMT) 数据显著改善了紧急病人的优先级. 这种人工智能驱动的方法实现了98%的准确性,优于当前的方法,并减少了医院的拥堵.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 远程医疗远程医疗
背景情况:
- 医疗物联网 (IoMT) 能够实现远程患者监控,但会产生大量数据,使紧急患者的优先顺序变得复杂.
- 现有的分类方法很难有效地管理来自集成医疗保健设备的数据流入.
研究的目的:
- 通过实施实时选优化框架 (RTOF) 来提高紧急病人的优先级.
- 利用各种 IoMT 数据,改善紧急护理中的实时决策.
主要方法:
- 利用各种IoMT数据,包括传感器读数和电子病历 (EMR).
- 将五种机器学习算法应用于10万名高血压和心脏病患者的数据集.
- 开发了实时选优化框架 (RTOF) 用于数据处理和分析.
主要成果:
- 在模拟的远程医疗环境中,RTOF实现了98%的分组准确率.
- 随机森林算法以98%的准确性,99%的精度,98%的灵敏性和100%的特异性展示了卓越的性能.
- 与现有的分拣方法相比,观察到显著的改善.
结论:
- RTOF超越了当前的分拣框架,提高了远程医疗的质量和有效性.
- 该框架为医院拥堵和实时资源分配提供了一个可扩展的解决方案.
- RTOF可以缓解过度拥挤,加快干预,并支持可适应的远程医疗网络.
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