TABEHR: BRIDGING LONGITUDINAL EHRS AND TABULAR FOUNDATION MODELS

Longitudinal electronic health records (EHRs) are heterogeneous and irregular: patients differ in which clinical concepts are recorded, when they are observed, and how often they occur, and these recording patterns also vary across institutions. This makes it difficult to reuse clinical prediction models across sites. Tabular foundation models (TFMs) offer reusable in-context prediction from labeled support patients without parameter updates, but require corresponding feature columns that irregular EHR histories do not provide. We introduce TabEHR, which learns this missing interface by mapping irregular histories into shared semantic columns for a frozen TFM. TabEHR constructs clinically enriched concept embeddings from ontology-informed descriptions synthesized through four complementary clinical perspectives and uses their geometry to define fixed semantic anchors that aggregate patient events into column-specific embeddings. Keeping the anchors and TFM fixed, we train TabEHR on MIMIC-IV to learn how patient events populate these shared columns. TabEHR achieves the highest macro-averaged AUROC and AUPRC across nine tasks among the evaluated methods, both on held-out MIMIC-IV patients and on the external EHRSHOT cohort, without using EHRSHOT outcome labels.