Description
This pretrained pipeline is built on the top of ner_deid_subentity model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
text = """Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."""
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
val result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:----------------------|--------:|------:|:------------|:-------------|
| 0 | Gastone Montanariello | 9 | 29 | PATIENT | |
| 1 | 49 | 32 | 33 | AGE | |
| 2 | Ospedale San Camillo | 55 | 74 | HOSPITAL | |
| 3 | marzo 2015 | 128 | 137 | DATE | |
Model Information
| Model Name: | ner_deid_subentity_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | it |
| Size: | 1.3 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel