<h2>GatorTron-Medium overview </h2>

Developed by a joint effort between the University of Florida and NVIDIA, GatorTron-Medium is a clinical language model of 3.9 billion parameters, pre-trained using a BERT architecure implemented in the Megatron package (https://github.com/NVIDIA/Megatron-LM).

GatorTron-Medium is pre-trained using a dataset consisting of:

The Github for GatorTron is at : https://github.com/uf-hobi-informatics-lab/GatorTron

<h2>Model variations</h2>

Model Parameter
gatortron-base 345 million
gatortronS 345 million
gatortron-medium (this model) 3.9 billion
gatortron-large 8.9 billion

<h2>How to use</h2>

from transformers import AutoModel, AutoTokenizer, AutoConfig

tokinizer= AutoTokenizer.from_pretrained('UFNLP/gatortron-medium')
config=AutoConfig.from_pretrained('UFNLP/gatortron-medium')
mymodel=AutoModel.from_pretrained('UFNLP/gatortron-medium')

encoded_input=tokinizer("Bone scan:  Negative for distant metastasis.", return_tensors="pt")
encoded_output = mymodel(**encoded_input)

<h2>De-identification</h2>

We applied a de-identification system to remove protected health information (PHI) from clinical text. We adopted the safe-harbor method to identify 18 PHI categories defined in the Health Insurance Portability and Accountability Act (HIPAA) and replaced them with dummy strings (e.g., replace people’s names into [**NAME**]).

The de-identifiation system is described in:

Yang X, Lyu T, Li Q, Lee C-Y, Bian J, Hogan WR, Wu Y†. A study of deep learning methods for de-identification of clinical notes in cross-institute settings. BMC Med Inform Decis Mak. 2020 Dec 5;19(5):232. https://www.ncbi.nlm.nih.gov/pubmed/31801524.

<h2>Citation info</h2>

Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, Compas C, Martin C, Costa AB, Flores MG, Zhang Y, Magoc T, Harle CA, Lipori G, Mitchell DA, Hogan WR, Shenkman EA, Bian J, Wu Y†. A large language model for electronic health records. Npj Digit Med. Nature Publishing Group; . 2022 Dec 26;5(1):1–9. https://www.nature.com/articles/s41746-022-00742-2

@article{yang2022large,
  title={A large language model for electronic health records},
  author={Yang, Xi and Chen, Aokun and PourNejatian, Nima and Shin, Hoo Chang and Smith, Kaleb E and Parisien, Christopher and Compas, Colin and Martin, Cheryl and Costa, Anthony B and Flores, Mona G and Zhang, Ying and Magoc, Tanja and Harle, Christopher A and Lipori, Gloria and Mitchell, Duane A and Hogan, William R and Shenkman, Elizabeth A and Bian, Jiang and Wu, Yonghui },
  journal={npj Digital Medicine},
  volume={5},
  number={1},
  pages={194},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

<h2>Contact</h2>