Intensive Care Units usually carry patients with a serious risk of mortality. Recent research has shown the ability of Machine Learning to indicate the patients' mortality risk and point physicians toward individuals with a heightened need for care. Nevertheless, healthcare data is often subject to privacy regulations and can therefore not be easily shared in order to build Centralized Machine Learning models that use the combined data of multiple hospitals. Federated Learning is a Machine Learning framework designed for data privacy that can be used to circumvent this problem. In this study, we evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage. We compare the predictive performance of Federated, Centralized, and Local Machine Learning in terms of AUPRC, F1-score, and AUROC. Our results show that Federated Learning performs equally well as the centralized approach and is substantially better than the local approach, thus providing a viable solution for early Intensive Care Unit mortality prediction. In addition, we show that the prediction performance is higher when the patient history window is closer to discharge or death. Finally, we show that using the F1-score as an early stopping metric can stabilize and increase the performance of our approach for the task at hand.
翻译:最近的研究显示,机器学习能够显示病人的死亡率风险,并将医生指向高度需要护理的个人,然而,保健数据往往受到隐私条例的制约,因此不能轻易分享,以建立使用多家医院综合数据的中央化机械学习模式。联邦学习是一个用于数据隐私的机械学习框架,可以用来规避这一问题。在本研究中,我们评估了深联邦学习在早期预测密集护理单位死亡率风险的能力。我们比较了联邦、中央化和地方机构学习的预测性能,以AUPRC、F1核心和AUROC为基准。我们的结果显示,联邦学习同样以集中的方式运作,而且大大优于地方方法,从而为早期强化护理单位死亡率预测提供了可行的解决办法。此外,我们表明,在病人历史窗口接近排放或死亡时,预测性能更高。最后,我们显示,用F1核心作为早期停止指标可以稳定并增加我们执行任务的业绩。