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 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 791.6 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter