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}
}
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Evaluation results
- NER Precisionself-reported0.896
- NER Recallself-reported0.900
- NER F Scoreself-reported0.898