Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
Stud Health Technol Inform. 2024 Aug 22;316:786-790. doi: 10.3233/SHTI240529.
This study explores the potential of federated learning (FL) to develop a predictive model of hypoxemia in intensive care unit (ICU) patients. Centralized learning (CL) and local learning (LL) approaches have been limited by the localized nature of data, which restricts CL approaches to the available data due to data privacy regulations. A CL approach that combines data from different institutions, could offer superior performance compared to a single-institution approach. However, the use of this method raises ethical and regulatory concerns. In this context, FL presents a promising middle ground, enabling collaborative model training on geographically dispersed ICU data without compromising patient confidentiality. This study is the first to use all five public ICU databases combined. The findings demonstrate that FL achieved comparable or even slightly improved performance compared to local or centralized learning approaches.
这项研究探讨了联邦学习（FL）在开发重症监护病房（ICU）患者低氧血症预测模型方面的潜力。集中式学习（CL）和局部学习（LL）方法受到数据局部性的限制，由于数据隐私法规，CL 方法仅限于可用数据。与单一机构方法相比，结合来自不同机构的数据的 CL 方法可能会提供更好的性能。但是，这种方法的使用引发了伦理和监管方面的问题。在这种情况下，FL 提供了一个有前途的中间地带，能够在不损害患者隐私的情况下，对地理上分散的 ICU 数据进行协作模型训练。本研究首次使用了全部五个公共 ICU 数据库的组合。研究结果表明，FL 的性能与局部或集中式学习方法相当，甚至略有提高。
