|
--- |
|
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 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: |
|
|
|
- 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 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: |
|
|
|
- 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") |
|
``` |
|
|
|
*Note: Because of our distinct model architecture, you are required to train a distinct predictor or use a respective fully-connected network above the hidden state when deploying the model via `transformers`* |
|
|
|
## 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 |
