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}
}
