README.md

---
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

Also check out [`cja5553/BJH-perioperative-notes-bioGPT`](https://huggingface.co/cja5553/BJH-perioperative-notes-bioGPT), which is the bioGPT variant of our model!

## Dataset

We used 84,875 perioperative clinical notes from patients spanning the Barnes Jewish Healthcare (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

```python
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