通过区域计算优化医疗保健大数据性能
Tariq Alsahfi1, Afzal Badshah2, Omar Ibrahim Aboulola3
1Department of Information Systems and Technology, University of Jeddah, Jeddah, Saudi Arabia. tmalsahfi@uj.edu.sa.
Scientific reports
|January 24, 2025
概括
区域计算 (RC) 解决了医疗保健大数据 (HBD) 的挑战. 这种方法在区域范围内处理医疗数据,减少云延迟,实现实时分析和改善患者护理.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 医疗保健行业正在经历数字化转型,包括医疗物联网 (IOMT),电子健康记录 (EHR) 和可穿戴设备等技术.
- 这种数字化转变产生了大量的医疗保健大数据 (HBD),需要高效的分析来改善患者的治疗结果和护理.
- 传统的基于云计算的处理面临延迟和网络拥堵问题,大型的,时间敏感的HBD,阻碍实时应用程序.
研究的目的:
- 提出一个区域计算 (RC) 范式来管理医疗保健大数据 (HBD).
- 为了减轻与HBD集中的云处理相关的延迟和网络拥堵挑战.
- 实现及时,实时的数据分析,以加强医疗保健决策.
主要方法:
- 该研究提出了一个区域计算 (RC) 框架.
- 该框架利用战略位置的区域服务器进行本地化数据收集,处理和存储.
- 该RC方法旨在减少对集中式云基础设施的依赖,特别是在高峰负载期间.
主要成果:
- 该RC范式有效地减少了医疗保健大数据 (HBD) 处理的延迟.
- 区域化数据管理促进了地方一级的实时分析.
- 该框架减轻了传统云计算对时间敏感的医疗数据所施加的限制.
结论:
- 区域计算 (RC) 为管理医疗保健大数据 (HBD) 挑战提供了可行的解决方案.
- 这种方法提高了医疗保健提供者利用实时数据进行个性化和优化患者护理的能力.
- RC赋予了数据驱动的决策能力,从而改善了诊断,监测和外科干预.
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