马巴融合用于隐私保护疾病预测
Muhammad Kashif Jabbar1,2, Huang Jianjun3,4, Ayesha Jabbar1,2
1Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China.
Scientific reports
|July 2, 2025
概括
曼巴-Fusion 通过保护隐私的多模式数据分析来提高疾病预测. 这种联合学习 (FL) 方法可以实现高准确性,同时最大限度地降低通信成本并保护敏感的患者信息.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 准确的疾病预测至关重要,但受到限制数据共享的隐私法规 (GDPR,HIPAA) 的阻碍.
- 现有的联合学习 (FL) 和多模式融合方法在可扩展性,通信性和数据异质性方面面临挑战.
- 隐私保护技术往往会损害模型性能或增加计算负担.
研究的目的:
- 引入Mamba-Fusion,这是一个用于多模式疾病预测的新型隐私保护框架.
- 在可扩展性,通信效率和数据隐私方面解决当前FL和融合技术的局限性.
- 为了实现安全,大规模的协作医疗分析.
主要方法:
- 一个层次化的联合学习 (FL) 架构,以降低通信成本并提高可扩展性.
- 专家混合 (MoE) 与基于LSTM的层面,用于多模式数据的动态时间集成.
- 集成先进的隐私技术,如差异隐私和安全聚合.
主要成果:
- 与传统的FL技术相比,Mamba-Fusion表现出更高的性能.
- 在多模式临床数据 (心电图,脑电图,笔记,人口统计) 上,获得了92.4%的准确性,0.91的F-Score和0.96的AUC-ROC.
- 保持低隐私泄露率 (0.02) 和通信成本 (12.5 MB).
结论:
- 马巴-Fusion提供了一个可扩展的,保护隐私的解决方案,用于多模式疾病预测.
- 该框架有效地平衡了数据保护与高预测准确性.
- Mamba-Fusion支持安全,大规模的协作医疗分析.
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