Description
This pipeline extracts mentions of clinical departments and medical devices from health-related text in colloquial language.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_vop_clinical_dept_pipeline", "en", "clinical/models")
pipeline.annotate("
My little brother is having surgery tomorrow in the orthopedic department. He is getting a titanium plate put in his leg to help it heal faster. Wishing him a speedy recovery!
")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_vop_clinical_dept_pipeline", "en", "clinical/models")
val result = pipeline.annotate("
My little brother is having surgery tomorrow in the orthopedic department. He is getting a titanium plate put in his leg to help it heal faster. Wishing him a speedy recovery!
")
Results
| chunk | ner_label |
|:----------------------|:--------------|
| orthopedic department | ClinicalDept |
| titanium plate | MedicalDevice |
Model Information
| Model Name: | ner_vop_clinical_dept_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 791.7 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter