Clinical NER (Large)

Description

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.

Live Demo Open in Colab Download

How to use


ner = NerDLModel.pretrained("ner_clinical_large", "en") \
        .setInputCols(["document", "token", "embeddings"]) \
        .setOutputCol("ner")

val ner = NerDLModel.pretrained("ner_clinical_large", "en")
        .setInputCols(Array("document", "token", "embeddings"))
        .setOutputCol("ner")

Model Information

Model Name: ner_clinical_large
Type: ner
Compatibility: Spark NLP for Healthcare 2.5.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en
Case sensitive: false

Data Source

The model is imported from https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/