Volatility-Aware Masking Improves Performance and Efficiency of Pretrained EHR Foundation Models

Masked autoencoder (MAE) models are increasingly applied to electronic health records (EHR) as a pre-training method to learn general-purpose representations that support diverse downstream clinical tasks. However, existing approaches typically rely on uniform random masking, implicitly assuming that all clinical features are equally predictable. In practice, laboratory tests exhibit substantial heterogeneity in temporal volatility: certain biomarkers (e.g., sodium) remain relatively stable, whereas others (e.g., lactate) fluctuate considerably and are more challenging to model. To address this limitation, we propose Volatility-Aware Masking strategy (CV-Masking), a pretraining strategy that adaptively adjusts masking probabilities according to the intrinsic variability of each feature. Our experiments on a large panel of laboratory tests demonstrate that CV-Masking consistently outperforms both random and variance-based masking strategies, yielding improved downstream predictive performance and faster convergence.