Description
This pretrained pipeline is built on the top of ner_oncology_demographics model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
text = '''The patient is a 40-year-old man with history of heavy smoking.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
val text = "The patient is a 40-year-old man with history of heavy smoking."
val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
text = '''The patient is a 40-year-old man with history of heavy smoking.'''
result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:--------------|--------:|------:|:---------------|-------------:|
| 0 | 40-year-old | 17 | 27 | Age | 0.6743 |
| 1 | man | 29 | 31 | Gender | 0.9365 |
| 2 | heavy smoking | 49 | 61 | Smoking_Status | 0.7294 |
Model Information
| Model Name: | ner_oncology_demographics_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel