Description
This pipeline is designed to extract all entities mappable to 4 major categories of UMLS CUI codes.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("ner_umls_major_concepts_pipeline", "en", "clinical/models")
result = ner_pipeline.annotate("""A female patient got influenza vaccine and one day after she has complains of ankle pain.
She has only history of gestational diabetes mellitus diagnosed prior to presentation and subsequent type two diabetes mellitus (T2DM).""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("ner_umls_major_concepts_pipeline", "en", "clinical/models")
val result = ner_pipeline.annotate("""A female patient got influenza vaccine and one day after she has complains of ankle pain.
She has only history of gestational diabetes mellitus diagnosed prior to presentation and subsequent type two diabetes mellitus (T2DM).""")
Results
| | chunks | begin | end | entities |
|---:|:------------------------------|--------:|------:|:-------------|
| 0 | influenza vaccine | 21 | 37 | Vaccine_Name |
| 1 | one day after | 43 | 55 | RelativeDate |
| 2 | ankle pain | 78 | 87 | Symptom |
| 3 | gestational diabetes mellitus | 115 | 143 | Diabetes |
| 4 | type two diabetes mellitus | 192 | 217 | Diabetes |
| 5 | T2DM | 220 | 223 | Diabetes |
Model Information
Model Name: | ner_umls_major_concepts_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
- MedicalNerModel
- NerConverterInternalModel