FedComDist: Towards Effective Personalized Federated Learning for Patient Outcome Prediction Using Multi-Center Electronic Medical Records

Accurate patient outcome predictions are essential for healthcare improvement, yet utilizing diverse medical data raises privacy and security concerns. Federated learning enables collaborative model training while preserving data privacy. However, the heterogeneity of clinical features among hospitals poses a challenge, leading to suboptimal performance in conventional federated learning. In response, we propose FedComDist, a personalized federated learning approach designed to maximize the use of heterogeneous features across hospitals for patient outcome prediction. Our approach incorporates a novel method for optimizing common global and distinct local parameters. We categorize input clinical features into two main groups—common and distinct—based on their presence across all hospitals and decouple the model parameters into common and distinct accordingly. The common features are used to train common global parameters, which are aggregated and optimized on the server, making them trainable across all hospitals. Meanwhile, the distinct features are used to train local parameters and optimized using the local dataset of each hospital. Our approach is evaluated on the eICU dataset, a publicly available multi-center clinical dataset, to predict patient clinical outcomes, specifically mortality and Length of Stay (LoS). The experimental results demonstrate the effectiveness of our approach compared to various federated learning methods and provide enhanced privacy through parameter decoupling.