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Breast Cancer Diagnosis NER model

Feature Description
Name es_BreastCancerNER
Version 0.0.0
spaCy >=3.5.0,<3.6.0
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 (21 labels for 1 components)
Component Labels
ner CANCER_CONCEPT, CANCER_EXP, CANCER_GRADE, CANCER_INTRTYPE, CANCER_LOC, CANCER_MET, CANCER_REC, CANCER_STAGE, CANCER_SUBTYPE, CANCER_TYPE, DATE, IMPLICIT_DATE, MOLEC_MARKER, SURGERY, TNM, TRAT, TRAT_DRUG, TRAT_FREQ, TRAT_INTERVAL, TRAT_QUANTITY, TRAT_SHEMA

Accuracy

Type Score
ENTS_F 93.21
ENTS_P 92.46
ENTS_R 93.97
TRANSFORMER_LOSS 45014.63
NER_LOSS 1216054.67

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}
}
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Evaluation results