基于联合学习的未来生物医学大数据分析和标准化.
Afifa Salsabil Fathima1, Syed Muzamil Basha1, Syed Thouheed Ahmed2
1School of Computer Science and Engineering, REVA University, Bengaluru, India.
PloS one
|October 4, 2023
概括
联合学习 (FL) 通过标准化和标记数据集来增强医疗数据分析. 这种方法在远程医疗数据集群中实现了97.34%的准确性,改善了生物医学决策支持.
科学领域:
- 生物医学数据科学 生物医学数据科学
- 医疗保健中的机器学习
- 数据隐私和安全数据隐私和安全
背景情况:
- 医疗数据处理对于生物医学决策支持至关重要,但数据敏感性需要专门的框架.
- 医疗大数据的不同来源需要强大的方法来确定来源,属性划分和特征提取.
- 现有的框架可能缺乏有效的定制监督和以应用为中心的敏感医疗信息处理.
研究的目的:
- 构想一个联合学习 (FL) 框架,用于分析和统一各种医疗数据集.
- 使用FL模型建立基于属性的特征绘图和集群分类的方法.
- 为生物医学应用中数据集标准化和标签提供坚定的补救措施.
主要方法:
- 实施四层架构:数据来源,获取,分类和优化层.
- 使用多目标最佳数据集 (MooM) 进行属性驱动的特征绘图和通过FL的集群分类.
- 在多个神经网络层之间协调功能同步和参数提取.
主要成果:
- 拟议的FL技术在分离和聚类远程医疗数据方面表现出高效.
- 在数据聚类中实现了97.34%的令人印象深刻的准确率.
- 联邦模型中的操作服务器促进了高效的数据处理和分析.
结论:
- 联合学习提供了一个强大的解决方案,用于分析敏感的医疗大数据,同时保持隐私.
- 拟议的框架有效地标准化和标记数据集,增强它们对生物医学应用的实用性.
- 取得的准确性突显了FL在改进远程医疗和远程医疗之外的决策支持系统方面的潜力.
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