Federated Learning-Driven Health Risk Prediction on Electronic Health Records Under Privacy Constraints

This study addresses the challenges of privacy protection and data silos in the intelligent analysis and health risk prediction of electronic health records by proposing a federated learning-based framework. In this framework, data from different medical institutions do not require centralized storage but instead achieve cross-institutional collaborative optimization through local model training and secure parameter aggregation, thereby improving model performance under conditions of compliance and privacy protection. A multimodal feature fusion mechanism is introduced to jointly model structured diagnostic information, clinical text, and time-series data, enabling the capture of complex semantic associations and temporal relationships across modalities. At the output stage, the model adopts a probabilistic prediction strategy optimized with cross-entropy loss, which effectively enhances the accuracy and stability of risk identification. Experiments conducted on a public electronic health record dataset show that the proposed method outperforms several baseline models in accuracy, precision, recall, and F1-Score, achieving a good balance between privacy protection and predictive robustness. Overall, this study establishes an integrated framework that combines federated learning with multimodal modeling, providing a feasible path for the efficient use of electronic health records and health risk prediction, while demonstrating significant advantages in improving the value of medical data and supporting better health management decisions.