DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-World Health System

Abstract

Accurate disease trajectory prediction is critical for early intervention, resource allocation, and improving long-term outcomes. While electronic health records (EHRs) provide a rich longitudinal view of patient health in clinical environments, models trained on curated research cohorts may not reflect routine deployment settings, and those trained on single-hospital datasets capture only fragments of each patient's trajectory. This highlights the importance of leveraging large, multi-hospital health systems for training and validation to better reflect real-world clinical complexity. In this work, we develop DT-Transformer, a foundation model trained on 57.1M structured EHR entries over 1.7M patients from Mass General Brigham (MGB), spanning 11 hospitals and a broad network of outpatient clinics. DT-Transformer achieves strong discrimination in both held-out and prospective validation settings. Next-event prediction achieves a median age- and sex-stratified AUC of 0.871 across 896 disease categories, with all categories exceeding AUC 0.5. These results support health system-scale training as a path toward foundation models suited to real-world clinical forecasting.

Contribution highlights

• In this work, we develop DT-Transformer, a foundation model trained on 57.1M structured EHR entries over 1.7M patients from Mass General Brigham (MGB), spanning 11 hospitals and a broad network of outpatient clinics.

Extracted from the public abstract; no claims have been added.

Code and data

Publication metadata

Version relationship

• Preprint: https://arxiv.org/abs/2605.14227

BibTeX

@article{Zhu2026Dttransformer,
  title = {DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-World Health System},
  author = {Yunying Zhu and Andrew R Weckstein and Kueiyu Joshua Lin and Jie Yang},
  journal = {arXiv preprint},
  year = {2026},
  url = {https://arxiv.org/abs/2605.14227}
}