Description
This pipeline extracts mentions of demographic information from health-related text in colloquial language.
Predicted Entities
Age, Employment, Gender, RaceEthnicity, RelationshipStatus, Substance, SubstanceQuantity
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_vop_demographic_pipeline", "en", "clinical/models")
pipeline.annotate("
My grandma, who's 85 and Black, just had a pacemaker implanted in the cardiology department. The doctors say it'll help regulate her heartbeat and prevent future complications.
")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_vop_demographic_pipeline", "en", "clinical/models")
val result = pipeline.annotate("
My grandma, who's 85 and Black, just had a pacemaker implanted in the cardiology department. The doctors say it'll help regulate her heartbeat and prevent future complications.
")
Results
| chunk | ner_label |
|:--------|:--------------|
| grandma | Gender |
| 85 | Age |
| Black | RaceEthnicity |
| doctors | Employment |
| her | Gender |
Model Information
| Model Name: | ner_vop_demographic_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