Description
This pipeline is designed to extract all entities mappable to SNOMED Drug codes.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("ner_snomed_drug_pipeline", "en", "clinical/models")
result = ner_pipeline.annotate("""
John's doctor prescribed aspirin for his heart condition, along with paracetamol for his fever and headache, amoxicillin for his tonsilitis and lansoprazole for his GORD on 2023-12-01.
""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("ner_snomed_drug_pipeline", "en", "clinical/models")
val result = ner_pipeline.annotate("""
John's doctor prescribed aspirin for his heart condition, along with paracetamol for his fever and headache, amoxicillin for his tonsilitis and lansoprazole for his GORD on 2023-12-01.
""")
Results
| | chunks | begin | end | entities |
|---:|:-------------|--------:|------:|:-----------|
| 0 | aspirin | 26 | 32 | DRUG |
| 1 | paracetamol | 70 | 80 | DRUG |
| 2 | amoxicillin | 110 | 120 | DRUG |
| 3 | lansoprazole | 145 | 156 | DRUG |
Model Information
| Model Name: | ner_snomed_drug_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.0.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- TextMatcherInternalModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMergeModel