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###VTE-BERT Hugging Face Model Usage Terms
The hosted AI model contains weights derived from datasets that may include Protected Health Information (PHI). Users must agree to the following:
- The model is provided strictly for research and development purposes.
- It must not be used for diagnostic, treatment, or clinical decision-making.
- Appropriate security and compliance measures must be implemented.
- Any model modifications must comply with AGPLv3.
- Reverse engineering or attempts to extract training data from the model are strictly prohibited.
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VTE-BERT Model Card
This repository contains a fine-tuned Bio_ClinicalBERT model (VTE-BERT) specialized for text classification tasks related to venous thromboembolism (VTE) in clinical narratives from patients diagnosed with cancer at Harris Health System (HHS) between 2011 and 2020.
Model Details
- Developed by: Ang Li Lab
- Base Model: Bio_ClinicalBERT
- Fine-tuning Dataset: Clinical notes of cancer patients from HHS (2011-2020)
- Task: Text classification
- Labels:
LE-DVT+PE+UE-DVTatypicalhistorynone
Intended Use
This model is intended for use in research settings to classify clinical text according to the aforementioned labels related to VTE outcomes in cancer patients. It may assist clinical researchers, informaticists, and healthcare professionals in efficiently analyzing large volumes of clinical notes.
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "ang-li-lab/VTE-BERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Your text here."
result = classifier(text)
print(result)
Access and Licensing
This model is available under gated access. Please review the TERMS.md file for terms and conditions of use.
The model code and data are subject to the LICENSE included in this repository.
Resources
- Demo: Demo Website
- Publication:
- GitHub: NLPMed-Engine, a robust and extensible NLP engine tailored for medical text, internally using VTE-BERT.
Contact
For questions or inquiries, please contact our lab through our lab website.
Citation
If you utilize this model in your research, please cite our paper:
@article{your_article_citation,
author = {Your Authors},
title = {Title of Your Paper},
journal = {Journal Name},
year = {Year},
volume = {Volume},
pages = {Pages},
doi = {DOI}
}
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