Detect Problems, Tests and Treatments (ner_crf)

Description

Named Entity recognition annotator allows for a generic model to be trained by CRF model Clinical NER (Large) is a Named Entity Recognition model that annotates text to find references to clinical events. The entities it annotates are Problem, Treatment, and Test. Clinical NER is trained with the ‘embeddings_clinical’ word embeddings model, so be sure to use the same embeddings in the pipeline.

Predicted Entities

Problem, Test, Treatment

Live Demo Open in Colab Download Copy S3 URI

How to use

model = NerCrfModel.pretrained("ner_crf","en","clinical/models")\
	.setInputCols("sentence","token","pos","word_embeddings")\
	.setOutputCol("ner")

val model = NerCrfModel.pretrained("ner_crf","en","clinical/models")
	.setInputCols("sentence","token","pos","word_embeddings")
	.setOutputCol("ner")

Model Information

Data Source

Trained with augmented version of i2b2 dataset withclinical_embeddings FILLUP