Description
This pretrained pipeline is built on the top of ner_eu_clinical_condition model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
text = "
La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
"
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
val text = "
La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
"
val result = pipeline.fullAnnotate(text)
Results
| | chunks | begin | end | entities | confidence |
|---:|:---------------------|--------:|------:|:-------------------|-------------:|
| 0 | cicatriz | 37 | 44 | clinical_condition | 0.9883 |
| 1 | dolor a la palpación | 121 | 140 | clinical_condition | 0.87025 |
| 2 | signos | 170 | 175 | clinical_condition | 0.9862 |
| 3 | irritación | 180 | 189 | clinical_condition | 0.9975 |
| 4 | hernias inguinales | 214 | 231 | clinical_condition | 0.7543 |
Model Information
| Model Name: | ner_eu_clinical_condition_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | es |
| Size: | 1.3 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel