Description
This pipeline is designed to extract all entities mappable to SNOMED (Body Structure) codes.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("ner_snomed_bodyStructure_pipeline", "en", "clinical/models")
result = ner_pipeline.annotate("""The patient is a 30-year-old female with a long history of insulin-dependent diabetes, type 2; coronary artery disease;
chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes;
who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities.
She did receive a course of Bactrim for 14 days for UTI.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("ner_snomed_bodyStructure_pipeline", "en", "clinical/models")
val result = ner_pipeline.annotate("""The patient is a 30-year-old female with a long history of insulin-dependent diabetes, type 2; coronary artery disease;
chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes;
who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities.
She did receive a course of Bactrim for 14 days for UTI.""")
Results
| | chunks | begin | end | entities |
|---:|:--------------------|--------:|------:|:-----------|
| 0 | coronary artery | 95 | 109 | BodyPart |
| 1 | renal | 129 | 133 | BodyPart |
| 2 | peripheral vascular | 150 | 168 | BodyPart |
| 3 | lower extremities | 301 | 317 | BodyPart |
Model Information
| Model Name: | ner_snomed_bodyStructure_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.0.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.8 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMergeModel