Relation Extraction Model Clinical

Description

Relation Extraction model based on syntactic features using deep learning

Predicted Relations

Temporal relations (BEFORE, AFTER, SIMULTANEOUS, BEGUN_BY, ENDED_BY, DURING, BEFORE_OVERLAP) between clinical events (ner_events_clinical)

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How to use

model = RelationExtractionModel.pretrained("re_temporal_events_enriched_clinical","en","clinical/models")\
	.setInputCols("word_embeddings","chunk","pos","dependency")\
	.setOutputCol("category")
val model = RelationExtractionModel.pretrained("re_temporal_events_enriched_clinical","en","clinical/models")
	.setInputCols("word_embeddings","chunk","pos","dependency")
	.setOutputCol("category")

Model Information

Name: re_temporal_events_enriched_clinical  
Type: RelationExtractionModel  
Compatibility: Spark NLP 2.5.5+  
License: Licensed  
Edition: Official  
Input labels: [word_embeddings, chunk, pos, dependency]  
Output labels: [category]  
Language: en  
Case sensitive: False  
Dependencies: embeddings_clinical  

Data Source

Trained on data gathered and manually annotated by John Snow Labs https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/