Description
This pipeline extracts mentions of tests and their results from health-related text in colloquial language.
Predicted Entities
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_vop_test_pipeline", "en", "clinical/models")
pipeline.annotate("
I went to the endocrinology department to get my thyroid levels checked. They ordered a blood test and found out that I have hypothyroidism, so now I'm on medication to manage it.
")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_vop_test_pipeline", "en", "clinical/models")
val result = pipeline.annotate("
I went to the endocrinology department to get my thyroid levels checked. They ordered a blood test and found out that I have hypothyroidism, so now I'm on medication to manage it.
")
Results
| chunk | ner_label |
|:---------------|:------------|
| thyroid levels | Test |
| blood test | Test |
Model Information
| Model Name: | ner_vop_test_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