metadata
license: mit
language:
- en
library_name: transformers
tags:
- medical
- healthcare
- clinical
- perioperative care
base_model: emilyalsentzer/Bio_ClinicalBERT
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_medicine/tree/main