Description
This pretrained pipeline is built on the top of ner_negation_uncertainty model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
val result = pipeline.fullAnnotate(text)
Results
|chunk |ner_label|
+------------------------------------------------------+---------+
|probable de |UNC |
|cirrosis hepática |USCO |
|no |NEG |
|conocida previamente |NSCO |
|no |NEG |
|se realizó paracentesis control por escasez de liquido|NSCO |
|susceptible de |UNC |
|ca basocelular perlado |USCO |
Model Information
| Model Name: | ner_negation_uncertainty_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | es |
| Size: | 318.7 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- RoBertaEmbeddings
- MedicalNerModel
- NerConverterInternalModel