Description
This pipeline extracts mentions of clinical problems 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_problem_pipeline", "en", "clinical/models")
pipeline.annotate("
I've been experiencing joint pain and fatigue lately, so I went to the rheumatology department. After some tests, they diagnosed me with rheumatoid arthritis and started me on a treatment plan to manage the symptoms.
")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_vop_problem_pipeline", "en", "clinical/models")
val result = pipeline.annotate("
I've been experiencing joint pain and fatigue lately, so I went to the rheumatology department. After some tests, they diagnosed me with rheumatoid arthritis and started me on a treatment plan to manage the symptoms.
")
Results
| chunk | ner_label |
|:---------------------|:------------|
| pain | Symptom |
| fatigue | Symptom |
| rheumatoid arthritis | Disease |
Model Information
| Model Name: | ner_vop_problem_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