LATEiimas/bert-base-robson-criteria-classification-ner-es

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The bert-base-robson-criteria-classification-ner-es is a Named Entity Recognition (NER) model for the Spanish language fine-tuned from the RoBERTa base model.

Model Details

Model Description

In the table below, we have outlined the entities set. Most entities are based on the obstetric variables described in the Robson Implementation Manual Robson Classification: Implementation Manual. However, we have added nine additional entities related to the use of antibiotics, uterotonics, dose, posology, complications, obstetric hemorrhage, the outcome of delivery (whether it was a vaginal birth or a cesarean section), and the personal information within the Electronic Health Records (EHRs).

Clinical entities set

No	Spanish Entity	English Entity	Obsetric variable
1	Parto nulípara	Nullipara labor	Parity
2	Parto multípara	Multipara labor
3	Cesárea previa (Si)	One or more Cesarean Section	Previous Cesarean Section
4	Cesárea previa (No)	None Cesarean Section
5	TDP espontáneo	Spontaneous labor	Onset of labour
6	TDP inducido	Induced labor
7	TDP No: cesárea programada	No labor, scheduled Cesarean Section
8	Embarazo único	Singleton pregnancy	Number of fetuses
9	Embarazo Múltiple	Multiple pregnancy
10	Edad < 37 semanas	Preterm pregnancy	Gestational age
11	Edad ≥ 37 semanas	Term pregnancy
12	Posición cefálica	Cephalic presentation	Fetal lie and presentation
13	Posición podálica	Breech presentation
14	Situación transversa	Transverse lie
15	Antibiótico	Antibiotic
16	Complicación	Complication
17	Dosis	Dose
18	Hemorragia Obstétrica	Obstetric Hemorrhage
19	Info personal	Personal Information
20	Posología	Posology
21	Tipo de resolución: parto	Delivery resolution: VB
22	Tipo de resolución: cesarea	Delivery resolution: CS
23	Uterotónico	Uterotonic

This model detects entities by classifying every token according to the IOB format:

['O', 'B-Antibiótico', 'I-Antibiótico', 'B-Cesárea previa (NO)', 'I-Cesárea previa (NO)', 'B-Cesárea previa (SI)', 'I-Cesárea previa (SI)', 'B-Complicación', 'I-Complicación', 'B-Dosis', 'I-Dosis', 'B-Edad < 37 semanas', 'I-Edad < 37 semanas', 'B-Edad >= 37 semanas', 'I-Edad >= 37 semanas', 'B-Embarazo múltiple', 'I-Embarazo múltiple', 'B-Embarazo único', 'I-Embarazo único', 'B-Hemorragia obstétrica', 'I-Hemorragia obstétrica', 'B-Info personal', 'I-Info personal', 'B-Parto multípara', 'I-Parto multípara', 'B-Parto nulípara', 'I-Parto nulípara', 'B-Posición cefálica', 'I-Posición cefálica', 'B-Posición podálica', 'I-Posición podálica', 'B-Posología', 'I-Posología', 'B-Situación transversa', 'I-Situación transversa', 'B-TDP No: cesárea programada', 'I-TDP No: cesárea programada', 'B-TDP espontáneo', 'I-TDP espontáneo', 'B-TDP inducido', 'I-TDP inducido', 'B-Tipo de resolución: cesárea', 'I-Tipo de resolución: cesárea', 'B-Tipo de resolución: parto', 'I-Tipo de resolución: parto', 'B-Uterotónico', 'I-Uterotónico']

🤝 Author

Created by Orlando Ramos.
This model is part of the organization's efforts LATEiimas.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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