Description
This advanced pipeline extracts human phenotype entities from clinical texts and utilizes the sbiobert_base_cased_mli Sentence Bert Embeddings to map these entities to their corresponding Human Phenotype Ontology (HPO) codes. It also returns associated codes from the following vocabularies for each HPO code: - MeSH (Medical Subject Headings)- SNOMED- UMLS (Unified Medical Language System ) - ORPHA (international reference resource for information on rare diseases and orphan drugs) - OMIM (Online Mendelian Inheritance in Man).
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("hpo_resolver_pipeline", "en", "clinical/models")
result = ner_pipeline.annotate("""She is followed by Dr. X in our office and has a history of severe tricuspid regurgitation. On 05/12/08, preserved left and right ventricular systolic function, aortic sclerosis with apparent mild aortic stenosis. She has previously had a Persantine Myoview nuclear rest-stress test scan completed at ABCD Medical Center in 07/06 that was negative. She has had significant mitral valve regurgitation in the past being moderate, but on the most recent echocardiogram on 05/12/08, that was not felt to be significant. She does have a history of significant hypertension in the past. She has had dizzy spells and denies clearly any true syncope. She has had bradycardia in the past from beta-blocker therapy.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("hpo_resolver_pipeline", "en", "clinical/models")
val result = ner_pipeline.annotate("""She is followed by Dr. X in our office and has a history of severe tricuspid regurgitation. On 05/12/08, preserved left and right ventricular systolic function, aortic sclerosis with apparent mild aortic stenosis. She has previously had a Persantine Myoview nuclear rest-stress test scan completed at ABCD Medical Center in 07/06 that was negative. She has had significant mitral valve regurgitation in the past being moderate, but on the most recent echocardiogram on 05/12/08, that was not felt to be significant. She does have a history of significant hypertension in the past. She has had dizzy spells and denies clearly any true syncope. She has had bradycardia in the past from beta-blocker therapy.""")
Results
+--------------------------+-----+---+---------+----------+--------------------------+--------------------------------------------------+
| chunk|begin|end|ner_label|resolution| description| all_codes|
+--------------------------+-----+---+---------+----------+--------------------------+--------------------------------------------------+
| tricuspid regurgitation| 67| 89| HP|HP:0005180| tricuspid regurgitation|MSH:D014262||SNOMED:111287006||UMLS:C0040961||O...|
| aortic stenosis| 197|211| HP|HP:0001650| aortic stenosis|MSH:D001024||SNOMED:60573004||UMLS:C0003507||OR...|
|mitral valve regurgitation| 373|398| HP|HP:0001653|mitral valve regurgitation|MSH:D008944||SNOMED:48724000||UMLS:C0026266,C35...|
| hypertension| 555|566| HP|HP:0000822| hypertension|MSH:D006973||SNOMED:24184005,38341003||UMLS:C00...|
| bradycardia| 655|665| HP|HP:0001662| bradycardia|MSH:D001919||SNOMED:48867003||UMLS:C0428977||OR...|
+--------------------------+-----+---+---------+----------+--------------------------+--------------------------------------------------+
Model Information
| Model Name: | hpo_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.2.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 2.2 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel