Scaling survival analysis in healthcare with federated survival forests: A comparative study on heart failure and breast cancer genomics

Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are often incomplete, censored, distributed, and confidential, especially in healthcare settings where privacy is critical. The scarcity of data can severely limit the scalability of survival models to distributed applications that rely on large data pools. Federated learning is a promising technique that enables machine learning models to be trained on multiple datasets without compromising user privacy, making it particularly well-suited for addressing the challenges of survival data and large-scale survival applications. Despite significant developments in federated learning for classification and regression, many directions remain unexplored in the context of survival analysis. In this work, we propose an extension of the Federated Survival Forest algorithm, called FedSurF++. This federated ensemble method constructs random survival forests in heterogeneous federations. Specifically, we investigate several new tree sampling methods from client forests and compare the results with state-of-the-art survival models based on neural networks. The key advantage of FedSurF++ is its ability to achieve comparable performance to existing methods while requiring only a single communication round to complete. The extensive empirical investigation results in a significant improvement from the algorithmic and privacy preservation perspectives, making the original FedSurF algorithm more efficient, robust, and private. We also present results on two real-world datasets - a heart failure dataset from the Lombardy HFData project and Fed-TCGA-BRCA from the Falmby suite - demonstrating the success of FedSurF++ in real-world healthcare studies. Our results underscore the potential of FedSurF++ to improve the scalability and effectiveness of survival analysis in distributed settings while preserving user privacy.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Survival analysis is a fundamental tool in medicine for modeling the time until an event of interest occurs. However, in real-world applications, survival data is often incomplete, censored, distributed, and confidential, posing challenges in scalability and privacy preservation. This paper proposes FedSurF++, an extension of the Federated Survival Forest algorithm, which addresses these challenges by constructing random survival forests in heterogeneous federations. The algorithm achieves comparable performance to existing methods while requiring only a single communication round, improving efficiency, robustness, and privacy preservation. Empirical investigations on real-world datasets demonstrate the success of FedSurF++ in healthcare studies, showcasing its potential for improving the scalability and effectiveness of survival analysis while preserving user privacy.