ehr

DT-Transformer is a foundation model trained on 57.1 million structured EHR entries from 1.7 million patients across 11 hospitals in the Mass General Brigham health system, achieving strong discrimination for next-event prediction across 896 disease categories.

This paper investigates the latent structure of multimodal embeddings from a masked autoencoder for pediatric sleep analysis. It shows that augmenting embeddings with geometric, topological, and clinical features improves prediction and calibration for sleep-related events.

COTCAgent is a hierarchical reasoning framework for longitudinal electronic health records that uses a probabilistic chain-of-thought completion approach, achieving 90.47% Top-1 accuracy on a self-built dataset and outperforming existing medical agents.

This paper presents a nationwide EHR-based chronic rhinosinusitis prediction model using demographic-stratified models and a hybrid feature-selection pipeline, achieving an overall AUC of 0.8461 on data from the All of Us Research Program.

A 9-week pilot at a Dutch academic hospital shows 58% of admissions used LLM-generated discharge drafts, with 87% of clinicians reporting reduced documentation time and 91% intending continued use.