Abstract

Modern inpatient care generates irregular streams of heterogeneous clinical events, yet most predictive models require fixed feature matrices, predefined time windows, or discretization of continuous measurements. We developed CHaRT, a decoder-only autoregressive transformer designed to jointly forecast the identity of the next clinical event and, when applicable, its associated continuous value. CHaRT was trained and internally validated on structured electronic health record data from adult acute-care encounters across a 12-hospital health system in Minnesota from 2001 to 2025. The final corpus included 4,447,625 encounters from 1,301,502 patients and 701,556,877 non-padding clinical event tokens spanning vital signs, laboratory values, medications, diagnoses, microbiology, virology, imaging, fluids, and outcomes (ICU transfer or death). Encounters were split into training, validation, and test sets before vocabulary construction, normalization, and windowing. On the held-out test set, CHaRT achieved Top-1, Top-5, and Top-10 next-event accuracies of 51.61%, 87.34%, and 93.22%, respectively, with perplexity 4.50 and expected calibration error 0.0109. For numeric prediction, z-score MSE was 0.3812 for vital signs and 0.5713 for laboratory values. Seeded examples generated clinically coherent trajectories. Using model representations, a linear probe predicted deterioration (ICU transfer or in-hospital death) at a 6 h landmark with AUROC 0.95–0.97, indicating that learned representations transfer to downstream clinical risk prediction.

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