  Survival analysis serves as a fundamental component in numerous healthcare
applications, where the determination of the time to specific events (such as
the onset of a certain disease or death) for patients is crucial for clinical
decision-making. Scoring systems are widely used for swift and efficient risk
prediction. However, existing methods for constructing survival scores presume
that data originates from a single source, posing privacy challenges in
collaborations with multiple data owners. We propose a novel framework for
building federated scoring systems for multi-site survival outcomes, ensuring
both privacy and communication efficiency. We applied our approach to sites
with heterogeneous survival data originating from emergency departments in
Singapore and the United States. Additionally, we independently developed local
scores at each site. In testing datasets from each participant site, our
proposed federated scoring system consistently outperformed all local models,
evidenced by higher integrated area under the receiver operating characteristic
curve (iAUC) values, with a maximum improvement of 11.6%. Additionally, the
federated score's time-dependent AUC(t) values showed advantages over local
scores, exhibiting narrower confidence intervals (CIs) across most time points.
The model developed through our proposed method exhibits effective performance
on each local site, signifying noteworthy implications for healthcare research.
Sites participating in our proposed federated scoring model training gained
benefits by acquiring survival models with enhanced prediction accuracy and
efficiency. This study demonstrates the effectiveness of our privacy-preserving
federated survival score generation framework and its applicability to
real-world heterogeneous survival data.
