Unlocking Multi-Institutional Insights into Disease Progression Using Federated Learning on Longitudinal Electronic Health Records

Thomas M. Menino Convention & Exhibition Center 

Multisite longitudinal analyses of electronic health record (EHR) data can reveal real-world disease trajectories, but privacy constraints, high communication costs, outcome-specific missingness, and cross-site heterogeneity often prevent principled pooling and standard longitudinal modeling. In response, we propose PEAL (Privacy-preserving Efficient Aggregation for Longitudinal data), a single-round federated algorithm for multi-level linear mixed-effects models that yields estimates identical to pooled individual-level analysis. We then extend to MV-PEAL, a one-shot federated framework for multivariate mixed-effects models that reconstructs the global likelihood from pattern-specific summaries, enabling efficient estimation of fixed effects, covariance components, and cross-outcome correlations while accommodating outcome missingness in a secure manner. Extensive simulations show gold-standard-equivalent accuracy and improved efficiency over complete-case and standard imputation strategies. By applying the methods to the Johns Hopkins and University of Pittsburge systemic sclerosis cohorts, our methods recover clinically plausible single- and multi-biomarker trajectories, illustrating their utility for distributed research networks studying rare diseases and other time-evolving outcomes.

Federated Learning

Longitudinal Data

EHR Data

Data Integration