4 papers
Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding
Xiao Xiang, David Restrepo, Hyewon Jeong +2
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 unde…
Rethinking Tokenization for Clinical Time Series: When Less is More
Rafi Al Attrach, Rajna Fani, David Restrepo +2
Tokenization strategies shape how models process electronic health records, yet fair comparisons of their effectiveness remain limited. We present a systematic evaluation of tokeni…
Coefficient of Variation Masking: A Volatility-Aware Strategy for EHR Foundation Models
Rajna Fani, Rafi Al Attrach, David Restrepo +3
Masked autoencoders (MAEs) are increasingly applied to electronic health records (EHR) for learning general-purpose representations that support diverse clinical tasks. However, ex…
Representation Learning of Lab Values via Masked AutoEncoders
David Restrepo, Chenwei Wu, Yueran Jia +5
Accurate imputation of missing laboratory values in electronic health records (EHRs) is critical to enable robust clinical predictions and reduce biases in AI systems in healthcare…