This is a Transformer's
AutoModelForSequenceClassification trained for multilabel biomedical text classification in Spanish.
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.
This model is implemented as part of the KeyCARE library. Install first the keycare module to call the Transformer classifier:
1from keycare install TermExtractor.TermExtractor
2
3# initialize the termextractor object
4termextractor = TermExtractor(categorization_method='transformers')
5# Run the pipeline
6text = """Acude al Servicio de Urgencias por cefalea frontoparietal derecha.
7Mediante biopsia se diagnostica adenocarcinoma de próstata Gleason 4+4=8 con metástasis óseas múltiples.
8Se trata con Ácido Zoledrónico 4 mg iv/4 semanas.
9"""
10termextractor(text)
11# You can also access the class storing the Transformer model
12categorizer = termextractor.categorizer
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.
Click to expand
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.
