Alvaro8gb/es_neg_uncert_ehr_ner

Feature	Description
Name	es_neg_uncert_ehr_ner
Version	0.0.0
spaCy	>=3.7.2,<3.8.0
Default Pipeline	transformer, ner
Components	transformer, ner
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	n/a
License	mit
Author	Álvaro García Barragán

Label Scheme

View label scheme (4 labels for 1 components)

Component	Labels
ner	NEG, NSCO, UNC, USCO

Accuracy

Type	Score
ENTS_F	89.81
ENTS_P	89.65
ENTS_R	89.97
TRANSFORMER_LOSS	34598.52
NER_LOSS	35036.89

Citation

If you use our work in your research, please cite it as follows:

@INPROCEEDINGS{garcia-barraganCBMS2023,
  author={García-Barragán, Alvaro and Solarte-Pabón, Oswaldo and Nedostup, Georgiy and Provencio, Mariano and Menasalvas, Ernestina and Robles, Victor},
  booktitle={2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)},
  title={Structuring Breast Cancer Spanish Electronic Health Records Using Deep Learning},
  year={2023},
  pages={404-409},
  keywords={Natural Language Processing (NLP), Information extraction, Deep Learning, Breast cancer.},
  doi={10.1109/CBMS58004.2023.00252}
}

Installing

!pip install pip==22.0.2
!pip install https://huggingface.co/Alvaro8gb/es_neg_uncert_ehr_ner/resolve/main/es_neg_uncert_ehr_ner-any-py3-none-any.whl

Dataset

Corpus composed of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty.

@article{lima2020nubes,
  title={NUBes: A corpus of negation and uncertainty in Spanish clinical texts},
  author={Lima, Salvador and Perez, Naiara and Cuadros, Montse and Rigau, German},
  journal={arXiv preprint arXiv:2004.01092},
  year={2020}
}