biomedical-term-classifier

Biomedical term classifier with Transformers in Spanish

Table of contents

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• Model description
• Intended uses and limitations
• How to use
• Training
• Evaluation
• Additional information
  • Author
  • Licensing information
  • Citation information
  • Disclaimer

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Model description

This is a Transformer's AutoModelForSequenceClassification trained for multilabel biomedical text classification in Spanish.

Intended uses and limitations

The model is prepared to classify medical entities among 21 classes, including diseases, medical procedures, symptoms, and drugs, among others. It still lacks some classes like body structures.

How to use

This model is implemented as part of the KeyCARE library. Install first the keycare module to call the Transformer classifier:

python -m pip install keycare

You can then run the KeyCARE pipeline that uses the SetFit model:

from keycare install TermExtractor.TermExtractor

# initialize the termextractor object
termextractor = TermExtractor(categorization_method='transformers')
# Run the pipeline
text = """Acude al Servicio de Urgencias por cefalea frontoparietal derecha.
Mediante biopsia se diagnostica adenocarcinoma de próstata Gleason 4+4=8 con metástasis óseas múltiples.
Se trata con Ácido Zoledrónico 4 mg iv/4 semanas.
"""
termextractor(text)
# You can also access the class storing the Transformer model
categorizer = termextractor.categorizer

Training

The used pre-trained model is SapBERT-from-roberta-base-biomedical-clinical-es from the BSC-NLP4BIA reserch group. The model has been trained using data obtained from NER Gold Standard Corpora also generated by BSC-NLP4BIA, including MedProcNER, DISTEMIST, SympTEMIST, CANTEMIST, and PharmaCoNER, among others.

Evaluation

To be published

Additional information

Author

NLP4BIA at the Barcelona Supercomputing Center

Licensing information

Apache License, Version 2.0

Citation information

To be published

Disclaimer

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The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

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