# BJH-perioperative-notes-bioGPT

This clinical foundational model is intended to predict post-operative surgical outcomes from clinical notes taken during perioperative care. It was finetuned from the `microsoft/biogpt` 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 BioGptTokenizer, AutoModelForCausalLM
model=AutoModelForCausalLM.from_pretrained("cja5553/BJH-perioperative-notes-bioGPT")
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
```

## Codes
Codes used to train the model are publicly available at: https://github.com/cja5553/LLMs_in_medicine/tree/main 

## 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
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["medical", "clinical", "healthcare", "perioperative care"], "inference": false, "base_model": "microsoft/biogpt"}
transformers pytorch biogpt text-generation medical clinical healthcare perioperative care en base_model:microsoft/biogpt license:mit autotrain_compatible region:us