Description
This pretrained pipeline is built on the top of ner_oncology_anatomy_general_healthcare model.
Predicted Entities
Anatomical_Site, Direction
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
text = "
The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
"
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
val text = "
The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
"
val result = pipeline.fullAnnotate(text)
Results
| | chunks | begin | end | entities | confidence |
|---:|:---------|--------:|------:|:----------------|-------------:|
| 0 | left | 37 | 40 | Direction | 0.9948 |
| 1 | breast | 42 | 47 | Anatomical_Site | 0.5814 |
| 2 | lungs | 83 | 87 | Anatomical_Site | 0.9486 |
| 3 | liver | 100 | 104 | Anatomical_Site | 0.9646 |
Model Information
| Model Name: | ner_oncology_anatomy_general_healthcare_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 533.2 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel