Abstract
In-hospital mortality prediction is a crucial task in the clinical settings. Nevertheless, individual hospitals alone often have limited amount of local data to build a robust model. Usually domain transfer of an in-hospital mortality prediction model built with publicly-accessible dataset is conducted. The study in [6] shows quantitatively that with more datasets from different hospitals being shared, the generalizability and performance of domain transfer improves. We see this as an area that Federated Learning could help. It enables collaborative modelling to take place in a decentralized manner, without the need for aggregating all datasets in one place. This chapter reports a recent pilot of building an in-hospital mortality model with Federated Learning. It empirically shows that Federated Learning does achieve a similar level of performance with centralized training, but with additional benefit of no dataset exchanging among different hospitals. It also compares the performance of two common federated aggregation algorithms empirically in the Intensive Care Unit (ICU) setting, namely FedAvg and FedProx.
This work was done when author Nicholas Lim was attached to AI Singapore as an apprentice.
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Dang, T.K., Tan, K.C., Choo, M., Lim, N., Weng, J., Feng, M. (2020). Building ICU In-hospital Mortality Prediction Model with Federated Learning. In: Yang, Q., Fan, L., Yu, H. (eds) Federated Learning. Lecture Notes in Computer Science(), vol 12500. Springer, Cham. https://doi.org/10.1007/978-3-030-63076-8_18
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