Description
This pretrained pipeline is built on the top of ner_deid_subentity model.
Predicted Entities
MEDICALRECORD, ORGANIZATION, PROFESSION, DOCTOR, USERNAME, URL, CITY, DATE, SEX, PATIENT, SSN, COUNTRY, ZIP, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, AGE
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)
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)
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