通过创新的数据质量意识的联合分析来加强患者相似性网络
Alramzana Nujum Navaz1, Mohamed Adel Serhani2, Hadeel T El Kassabi3
1Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain P.O. Box 15551, United Arab Emirates.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
联合数据质量分析 (FDQP) 通过提高数据质量,增强了患者监测的边缘计算. 这种新的方法通过在源头评估数据完整性来确保准确的临床判断.
科学领域:
- 边缘计算 边缘计算
- 数据质量保证 数据质量保证
- 机器学习 机器学习
背景情况:
- 持续的患者监控产生了大量的感觉数据,需要边缘处理以提高效率和隐私.
- 由于传感器问题或传输问题,边缘的数据质量 (DQ) 退化可能会影响临床决策.
- 快速识别数据质量问题至关重要,以防止患者护理中的误解.
研究的目的:
- 建议联邦数据质量分析 (FDQP) 用于评估患者监测系统中边缘节点的数据质量.
- 开发FDQP的正式模型,捕捉数据质量维度并指导节点级数据质量保证.
- 利用联合学习原则进行高效和保护隐私的数据质量评估.
主要方法:
- 开发了联邦数据质量分析 (FDQP) 的正式模型,以捕获数据质量维度.
- 雇佣联合特征选择,按价值排列特征,异常值百分比和缺失数据百分比.
- 用分布在边缘节点上的胎儿数据集进行实验,以在各种场景下评估FDQP模型.
主要成果:
- 拟议的FDQP方法证明了边缘数据质量的显著改善.
- FDQP对基于联合患者相似性网络 (FPSN) 的机器学习模型的准确性产生了积极影响.
- 在FDQP中的轻量级配置文件交换实现了最佳的数据质量,与完整的数据处理相比,效率得到了提高.
结论:
- FDQP是评估和改善患者监控边缘计算环境中的数据质量的有效方法.
- 利用FDQP的数据质量意识的联合架构提高了机器学习模型的准确性.
- FDQP的方法显示出在患者监测之外的各种场景中应用的潜力.
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