This clinical foundational model is designed to predict potential risk factors / post-operative surgical outcomes from clinical notes taken during perioperative care. It was finetuned from the emilyalsentzer/Bio_ClinicalBERT model through a multi-task learning approach, spanning the following 6 outcomes:

Also check out cja5553/BJH-perioperative-notes-bioGPT, which is the bioGPT variant of our model!

We used 84,875 perioperative clinical notes spanning 3 years worth of anonymized patient data from the Barnes Jewish Healthcare (BJH) system in St Louis, MO. BJH is the largest hospital in the state of Missouri and the largest employer in the greater St. Louis region! The following are the characteristics for the data:

Codes used to train the model are publicly available at: https://github.com/cja5553/LLMs_in_perioperative_care

The model remains private as it was trained on clinical notes from patients. Requests made with non-institutional emails (e.g., gmail.com) will be rejected. Reasonable requests from recognized institutional emails may have to undergo appropriate approvals to access the model.

If you find this model useful, please cite the following paper:

@article{
author={Charles Alba, Bing Xue, Joanna Abraham, Thomas Kannampallil, Chenyang Lu},
title={The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes},
year={2025}, journal={npj Digital Medicine}, doi={10.1038/s41746-025-01489-2}
}

contact me at alba@wustl.edu