A study presents a federated deep learning approach for privacy-preserving cardiovascular disease risk prediction that integrates two population-based cohorts: Lifelines and the Rotterdam Study.
- The method combines 148,230 participants from Lifelines with self-reported outcomes and 10,155 participants from the Rotterdam Study with digitally linked clinical outcomes.
- Deep survival models trained using federated learning achieved higher predictive performance than models trained locally without federation.
- For the Rotterdam Study, the C-statistic increased from 0.728 to 0.739.
- For Lifelines, the C-statistic increased from 0.783 to 0.787.
These findings suggest that federated deep learning across heterogeneous cohorts can improve cardiovascular disease risk prediction while preserving the privacy of individual-level patient data.