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

sourceHugging Faceupdated 2y agoView on Hugging Face
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The roberta-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

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

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

python
['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']

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

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