Go to ML4H 2025 Symposium homepageCoefficient of Variation Masking: A Volatility-Aware Strategy for EHR Foundation Models
Keywords: Electronic Health Record, Masked Autoencoder, Masking Strategies, Foundation Models, Clinical AI
TL;DR: CV-based masking adapts pretraining to clinical reality by prioritizing volatile biomarkers over stable ones, improving EHR foundation model performance and training efficiency.
Track: Proceedings
Abstract: Masked autoencoders (MAEs) are increasingly applied to electronic health records (EHR) for learning general-purpose representations that support diverse clinical tasks. However, existing approaches typically rely on uniform random masking, implicitly assuming all features are equally predictable. In reality, laboratory tests exhibit substantial heterogeneity in volatility: some biomarkers (e.g., sodium) remain stable, while others (e.g., lactate) fluctuate considerably and are more difficult to model. Clinically, volatile biomarkers often signal acute pathophysiology and require more sophisticated modeling to capture their complex temporal patterns. We propose a volatility-aware pretraining strategy, Coefficient of Variation Masking (CV-Masking), that adaptively adjusts masking probabilities according to the intrinsic variability of each feature. Combined with a value-only masking objective aligned with clinical workflows, CV-Masking yields systematic improvements over random and variance-based strategies. Experiments on a large panel of laboratory tests show that CV-Masking enhances reconstruction, improves downstream predictive performance, and accelerates convergence, producing more robust and clinically meaningful EHR representations.
General Area: Models and Methods
Specific Subject Areas: Foundation Models, Representation Learning, Time Series, Unsupervised Learning
Data And Code Availability: No
Ethics Board Approval: No
Entered Conflicts: I confirm the above
Anonymity: I confirm the above
Submission Number: 258
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