metadata

license: mit
language:
  - en
library_name: transformers
tags:
  - medical
  - healthcare
  - clinical
  - perioperative care
base_model: emilyalsentzer/Bio_ClinicalBERT
inference: false

BJH-perioperative-notes-bioClinicalBERT

This clinical foundational model is intended to predict 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:

Death in 30 days
Deep vein thrombosis (DVT)
pulmonary embolism (PE)
Pneumonia
Acute Knee Injury
delirium

Dataset

We used 84,875 perioperative clinical notes from patients spanning the Barnes Jewish Hospital (BJH) system in St Louis, MO. The following are the characteristics for the data:

vocabulary size: 3203
averaging words per clinical note: 8.9 words
all single sentenced clinical notes

How to use model

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cja5553/BJH-perioperative-notes-bioClinicalBERT")
model = AutoModel.from_pretrained("cja5553/BJH-perioperative-notes-bioClinicalBERT")

Codes

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

Citation

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

@article{
author={Bing Xue, Charles Alba, Joanna Abraham, Thomas Kannampallil, Christopher King, Michael Avidan, Chenyang Lu}
title={"Prescribing Large Language Models for Perioperative Care: What’s The Right Dose for Pretrained Models?"},
year={2024}
}

Questions?

contact me at alba@wustl.edu