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arXiv · 2602.15159

Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding

Xiao Xiang·David Restrepo·Hyewon Jeong·Yugang Jia·Leo Anthony Celi
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Abstract

Electronic health records (EHR) arrive masked. Clinicians order measurements selectively, and any patient table thus contains only a subset of the values that characterize the underlying physiological state. Prior masked modeling approaches on EHR data either impute the table before learning, represent missingness through a dedicated placeholder signal, or optimize solely for imputation, which limits the representations they learn for downstream clinical tasks and carries every unobserved entry through the encoder. We introduce AID-MAE, an Augmented-Intrinsic Dual-Masked Autoencoder that learns directly from incomplete tables by combining the intrinsic mask the record already carries with an augmented mask that hides a subset of observed values for reconstruction during pretraining. Neither type of masked entry enters the encoder, so attention operates only over what was observed. AID-MAE achieves consistent improvements over strong baselines across multiple clinical tasks on two datasets. Across experiments, we discuss that recovering the missing entries is not a prerequisite for learning and show that the representations learned carry clinical structure without supervision.

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Xiao Xiang, David Restrepo, Hyewon Jeong, Yugang Jia, Leo Anthony Celi. 2026-02-16. Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding. https://arxiv.org/abs/2602.15159

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