客户端机器学习模型用于保护隐私的医疗预测 - 部署案例研究
Yacoub Abelard Njipouombe Nsangou1,2, Rajib Kumar Halder3, Ashraf Uddin4
1Dept. of Medical Bioinformatics, University Medical Center Göttingen, Germany.
Studies in health technology and informatics
|September 3, 2025
概括
这项研究通过直接在Web浏览器中运行医疗保健机器学习 (ML) 和深度学习 (DL) 模型的隐私保护方法. 这种客户端执行确保敏感的患者数据在用户的设备上保持安全.
科学领域:
- 计算科学
- 医疗信息学
- 机器学习
背景情况:
- 医疗保健中的传统以服务器为中心的机器学习 (ML) 和深度学习 (DL) 模型涉及向外部服务器传输敏感的患者数据,造成重大隐私风险.
- 像Flask这样的现有框架便于服务器端的处理,强调了需要更安全的替代方案.
研究的目的:
- 展示完全在Web浏览器中执行医疗预测模型的隐私保护方法.
- 消除与传统医疗保健ML/DL应用中的数据传输相关的隐私问题.
主要方法:
- 利用基于浏览器的ML/DL技术,包括TensorFlow.js和ONNX运行时Web.
- 基于模型复杂性的三种策略:简单模型的直接JavaScript,中等复杂模型的ONNX运行时Web (例如随机森林) 和复杂深度学习模型的TensorFlow.js (例如优化卷积神经网络).
- 确保所有计算都在用户设备上执行.
主要成果:
- 在客户端部署医疗预测模型是可行的和有效的.
- 在客户端执行时保留模型的原始性能指标.
- 通过将患者数据局部化来实现大量的隐私利益.
结论:
- 用户的设备上保证保留患者数据,从而减轻数据传输的风险.
- 这种方法对于优先考虑数据保密的医疗机构尤其有利.
- 客户端执行模型还支持医疗保健应用程序的离线功能.
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