Description
This pipeline extracts mentions of clinical problems from health-related text in colloquial language. All problem entities are merged into one generic Problem class.
Live Demo Open in Colab Copy S3 URI
Predicted entities
HealthStatus, Problem, Modifier
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_vop_problem_reduced_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_reduced_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 | Problem |
| fatigue | Problem |
| rheumatoid arthritis | Problem |
Model Information
| Model Name: | ner_vop_problem_reduced_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