机器学习方法的概述,使基于IoMT的发作检测能够实现
Alaa Lateef Noor Al-Hajjar1, Ali Kadhum M Al-Qurabat1
1Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq.
The Journal of supercomputing
|June 26, 2023
概括
医疗物联网 (IoMT) 和机器学习 (ML) 为早期发作检测提供了先进的解决方案. 这项技术可以实现远程监控,降低医疗保健成本并改善患者的治疗结果.
科学领域:
- 医疗保健技术 技术 医疗保健 技术
- 医疗信息学 医疗信息学
- 神经学 神经学
背景情况:
- 医疗保健行业越来越多地通过物联网 (IoT) 采用自动化.
- 医疗物联网 (IoMT) 专注于医学研究和应用.
- 是一个关键的全球卫生问题,需要有效的早期检测方法.
研究的目的:
- 审查与IoMT集成的尖端机器学习 (ML) 应用程序来检测.
- 突出IoMT,云服务和ML在应对医疗保健挑战中的作用.
- 强调早期发作检测的必要性,以防止死亡.
主要方法:
- 关于在IoMT中对的ML应用的当前文献的综述.
- 在IoMT框架内分析数据收集和处理.
- 将ML算法与IoMT集成,以提供增强的医疗保健解决方案.
主要成果:
- IoMT,云服务和ML是治疗等医疗保健问题的有效工具.
- 早期发现发作对于患者的生存和改善护理至关重要.
- 使用IoMT的远程医疗程序可以降低医疗保健费用并提高服务供应.
结论:
- 机器学习算法对于处理IoMT在医疗保健中生成的大量数据至关重要.
- IoMT和ML的组合为的检测和管理提供了一个强大的方法.
- 这些技术的进一步开发和整合有望在神经治疗中取得重大进展.
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