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
- NER Precisionself-reported0.925
- NER Recallself-reported0.940
- NER F Scoreself-reported0.932