软件质量注入 (QI):一种以质量为导向的整体方法,用于优化医疗保健大数据处理
Tia Haddad1, Pushpa Kumarapeli1, Simon de Lusignan2
1School of Computer Science and Mathematics, Kingston University London.
Studies in health technology and informatics
|April 9, 2025
概括
本研究介绍了一个云计算框架来管理医疗保健大数据 (BHD). 它提高了关键医疗保健应用的数据质量和系统弹性.
科学领域:
- 应用计算机应用计算
- 软件工程 软件工程 软件工程
- 医疗信息学 医疗信息学
背景情况:
- 大数据的扩张推动了软件开发的创新.
- 大型医疗数据 (BHD) 由于多结构和复杂的数据类型,提出了独特的挑战.
- 弹性和可扩展的架构对于解决BHD质量问题至关重要.
研究的目的:
- 为管理和优化医疗保健大数据 (BHD) 处理提出一个整体框架.
- 概述一种在关键医疗保健工作流程中确保最佳数据处理的方法.
- 提高BHD管理的系统质量属性.
主要方法:
- 采用先进的云计算策略.
- 实施BHD处理的整体框架.
- 利用复杂的编排功能来实时调整软件功能.
主要成果:
- 增强了BHD处理的系统质量属性.
- 改善了关键医疗保健工作流程的数据处理.
- 简化运营和增强系统弹性.
结论:
- 拟议的云计算框架有效地解决了BHD的挑战.
- 该框架优化了BHD处理,提高了数据质量和系统弹性.
- 这种方法支持医疗计算的先进分析和创新.
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