一个保护隐私的专家系统,用于跨多个机构的协作医疗诊断,使用联合学习
S Markkandan1, N P G Bhavani2, Srigitha S Nath3
1School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, Tamil Nadu, India. markkandan.s@vit.ac.in.
Scientific reports
|September 27, 2024
概括
本研究介绍了一种保护隐私的协作医学诊断方法,使用联合学习 (FL) 和高级加密. 这种方法提高了诊断准确度,同时保护了各机构的敏感患者数据.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 专家系统为医疗机构提供实时患者监测和诊断.
- 患者数据隐私是一个主要问题,泄露会造成财务和个人伤害.
- 现有的方法难以平衡诊断准确性和强大的数据保护.
研究的目的:
- 使用联合学习 (FL) 开发一种保护隐私的协作医学诊断 (CMD) 方法.
- 确保准确的患者数据分类,同时保持数据本地化和隐私.
- 解决多机构医疗保健系统中数据安全的挑战.
主要方法:
- 联合学习 (FL) 通过仅共享模型参数,可以在远程数据集上进行模型训练.
- 使用的是部分同型密码系统 (PHC) 和基于残留学习的深度信念网络 (RDBN) 的组合.
- 该方法可确保对患者生理数据进行安全和准确的分类.
主要成果:
- 拟议的CMD方法成功地保持了多个医疗机构的诊断准确性.
- 隐私受到有效保护,RDBN和PHC计算分别需要大约1000毫秒和150毫秒.
- 数据传输上的开支减少了30%,分类准确度平均增加了10%.
结论:
- 开发的保护隐私的CMD方法有效平衡诊断性能和患者数据安全.
- 联合学习与PHC和RDBN相结合,为安全的医疗诊断提供了强大的解决方案.
- 这种方法提高了医疗保健系统安全地进行实时监测和诊断的能力.
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