Description
This pipeline extracts mentions of treatment entities from health-related text in colloquial language.
## Predicted Entities
Frequency, Treatment, Drug, Route, Form, Dosage, Duration, Procedure
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_vop_treatment_pipeline", "en", "clinical/models")
pipeline.annotate("
My grandpa was diagnosed with type 2 diabetes and had to make some changes to his lifestyle. He also takes metformin and glipizide to help regulate his blood sugar levels. It's been a bit of an adjustment, but he's doing well.
")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_vop_treatment_pipeline", "en", "clinical/models")
val result = pipeline.annotate("
My grandpa was diagnosed with type 2 diabetes and had to make some changes to his lifestyle. He also takes metformin and glipizide to help regulate his blood sugar levels. It's been a bit of an adjustment, but he's doing well.
")
Results
| chunk | ner_label |
|:----------|:------------|
| metformin | Drug |
| glipizide | Drug |
Model Information
| Model Name: | ner_vop_treatment_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 791.6 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter