一种无损的一次性分布式算法,用于解决多站点通用线性模型中的异质性

Bingyu Zhang1,2, Qiong Wu1,3,4, Jenna M Reps5,6,7

  • 1The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, United States.

概括

我们为多机构通用线性模型 (GLM) 开发了一种保护隐私的算法. 这种方法可以实现来自异质来源的无损数据集成,而无需共享患者级信息,从而增强了协作研究.

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