i2b2-ner-model

i2b2 2018 Medical NER Model

This model is fine-tuned for medical Named Entity Recognition (NER) using the i2b2 2018 dataset.

Model Details

• Task: Named Entity Recognition (NER)
• Domain: Medical/Healthcare
• Dataset: i2b2 2018
• Model Type: Token Classification

Usage

from transformers import pipeline

# Load the model
ner_pipeline = pipeline(
    "ner",
    model="prakharsinghAI/i2b2-ner-model",
    aggregation_strategy="simple"
)

# Example usage
text = "Patient was prescribed aspirin 100mg twice daily for headache."
results = ner_pipeline(text)
print(results)

Training Details

• Dataset: i2b2 2018 Medical NER
• Task: Token Classification
• Labels: Medical entities (Drug, Dosage, Route, etc.)

Performance

This model was trained on the i2b2 2018 dataset for medical named entity recognition tasks.

Citation

If you use this model, please cite the i2b2 2018 dataset:

@article{krallinger2015chemdner,
  title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
  author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others},
  journal={Journal of cheminformatics},
  volume={7},
  number={1},
  pages={1--17},
  year={2015},
  publisher={BioMed Central}
}