Description
This pipeline extracts Morph Abnormality, Clinical Drug, Clinical Drug Form, Procedure, Substance, Physical Object, and Body Structure concepts from clinical notes, then maps them to their corresponding SNOMED codes using sbiobert_base_cased_mli Sentence Bert Embeddings.
How to use
from sparknlp.pretrained import PretrainedPipeline
resolver_pipeline = PretrainedPipeline("snomed_auxConcepts_resolver_pipeline", "en", "clinical/models")
result = resolver_pipeline.fullAnnotate("""This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, COPD, gastritis, and TIA. He initially presented to Braintree with a nonspecific ST-T abnormality and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of occlusion of the mid left anterior descending coronary artery lesion, which was complicated by hypotension and bradycardia. He required atropine, IV fluids, and dopamine, possibly secondary to a vagal reaction.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val resolver_pipeline = PretrainedPipeline("snomed_auxConcepts_resolver_pipeline", "en", "clinical/models")
val result = resolver_pipeline.fullAnnotate("""This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, COPD, gastritis, and TIA. He initially presented to Braintree with a nonspecific ST-T abnormality and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of occlusion of the mid left anterior descending coronary artery lesion, which was complicated by hypotension and bradycardia. He required atropine, IV fluids, and dopamine, possibly secondary to a vagal reaction.""")
Results
+-----------------------+---------------+-----------+-----------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+
| chunk| label|snomed_code| resolution| all_codes| all_resolutions| all_aux_labels|
+-----------------------+---------------+-----------+-----------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+
| tobacco| Smoking| 57264008| tobacco|57264008:::102407002:::39953003:::159882006:::1...|tobacco:::tobacco smoke:::tobacco - substance::...|Organism:::Substance:::Substance:::Social Conte...|
| nonspecific| Modifier| 10003008| non-specific|10003008:::261992003:::863956004:::300844001:::...|non-specific:::non-biological:::non-sterile:::n...|Qualifier Value:::Qualifier Value:::Qualifier V...|
|cardiac catheterization| Procedure| 41976001|cardiac catheterization|41976001:::705923009:::721968000:::467735004:::...|cardiac catheterization:::cardiac catheter:::ca...|Procedure:::Physical Object:::Record Artifact::...|
| atropine|Drug_Ingredient| 73949004| atropine|73949004:::105075009:::349945006:::410493009:::...|atropine:::atropine measurement:::oral atropine...|Pharma/Biol Product:::Procedure:::Clinical Drug...|
| fluids|Drug_Ingredient| 118431008| iv fluid|118431008:::82449006:::47625008:::261841005:::2...|iv fluid:::iv catheter:::iv route:::iv/c:::iv/r...|Substance:::Physical Object:::Qualifier Value::...|
| dopamine|Drug_Ingredient| 59187003| dopamine|59187003:::412383006:::37484001:::32779004:::41...|dopamine:::dopamine agent:::dopamine receptor::...|Pharma/Biol Product:::Substance:::Substance:::P...|
+-----------------------+---------------+-----------+-----------------------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+
Model Information
| Model Name: | snomed_auxConcepts_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 3.6 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- MedicalNerModel
- NerConverter
- MedicalNerModel
- NerConverter
- ChunkMergeModel
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel