论文标题
通用EHR联合学习框架
Universal EHR Federated Learning Framework
论文作者
论文摘要
联合学习（FL）是电子医疗记录（EHR）最实用的多源学习方法。尽管保证了隐私保护，但FL的广泛应用仍受到两个大挑战的限制：异质EHR系统和非I.I.D。数据特征。最近的一项研究提出了一个统一异质EHR的框架，名为Unihpf。我们试图通过结合UNIHPF和FL同时解决这两个挑战。我们的研究是将异质EHR统一为单个FL框架的第一种方法。与本地学习相比，这种组合平均可提供3.4％的性能增长。我们认为，我们的框架实际上适用于现实世界中的佛罗里达州。
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide application of FL is restricted by two large challenges: the heterogeneous EHR systems, and the non-i.i.d. data characteristic. A recent research proposed a framework that unifies heterogeneous EHRs, named UniHPF. We attempt to address both the challenges simultaneously by combining UniHPF and FL. Our study is the first approach to unify heterogeneous EHRs into a single FL framework. This combination provides an average of 3.4% performance gain compared to local learning. We believe that our framework is practically applicable in the real-world FL.
