ChunkResolver Snomed Findings Clinical

Description

Entity Resolution model Based on KNN using Word Embeddings + Word Movers Distance

Predicted Entities

Snomed Codes and their normalized definition with clinical_embeddings

Open in ColabDownload

How to use

model = ChunkEntityResolverModel.pretrained("chunkresolve_snomed_findings_clinical","en","clinical/models")\
	.setInputCols("token","chunk_embeddings")\
	.setOutputCol("entity")
val model = ChunkEntityResolverModel.pretrained("chunkresolve_snomed_findings_clinical","en","clinical/models")
	.setInputCols("token","chunk_embeddings")
	.setOutputCol("entity")

Model Information

Name: chunkresolve_snomed_findings_clinical  
Type: ChunkEntityResolverModel  
Compatibility: Spark NLP 2.5.1+  
License: Licensed  
Edition: Official  
Input labels: [token, chunk_embeddings ]  
Output labels: [entity]  
Language: en  
Case sensitive: True  
Dependencies: embeddings_clinical  

Data Source

Trained on SNOMED CT Findings http://www.snomed.org/