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Intensive Care Units usually carry patients with a serious risk of mortality.Recent research has shown the ability of Machine Learning to indicate thepatients' mortality risk and point physicians toward individuals with aheightened need for care. Nevertheless, healthcare data is often subject toprivacy regulations and can therefore not be easily shared in order to buildCentralized Machine Learning models that use the combined data of multiplehospitals. Federated Learning is a Machine Learning framework designed for dataprivacy that can be used to circumvent this problem. In this study, we evaluatethe ability of deep Federated Learning to predict the risk of Intensive CareUnit mortality at an early stage. We compare the predictive performance ofFederated, Centralized, and Local Machine Learning in terms of AUPRC, F1-score,and AUROC. Our results show that Federated Learning performs equally well asthe centralized approach and is substantially better than the local approach,thus providing a viable solution for early Intensive Care Unit mortalityprediction. In addition, we show that the prediction performance is higher whenthe patient history window is closer to discharge or death. Finally, we showthat using the F1-score as an early stopping metric can stabilize and increasethe performance of our approach for the task at hand.

Early prediction of the risk of ICU mortality with Deep Federated Learning