电子健康记录的联邦学习

Trung Kien Dang, Xiang Lan, J. Weng, Mengling Feng
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引用次数: 16

摘要

在数据驱动的医学研究中,由于单个机构有时没有足够的数据来获得足够的统计力来进行某些假设检验以及预测和亚组研究,因此长期以来,多中心研究比单中心研究更受青睐。电子健康记录(EHRs)的广泛采用使得多机构协作更加可行。然而,对基础设施、法规、隐私和数据标准化的担忧给医疗机构之间的数据共享带来了挑战。联邦学习(FL)允许多个站点在不直接共享数据的情况下协作训练全局模型,已成为打破数据隔离的一种有前途的范例。在本研究中,我们调查了FL在电子病历中应用的现有工作,并在真实世界的多中心电子病历数据集上评估了当前最先进的FL算法在两个具有重要临床意义的电子病历机器学习任务上的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Federated Learning for Electronic Health Records
In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy, and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.
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