Ner DL Model Clinical (Large)


Named Entity recognition annotator allows for a generic model to be trained by utilizing a deep learning algorithm (Char CNNs - BiLSTM - CRF - word embeddings) inspired on a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM,CNN. 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 DemoOpen in ColabDownload

How to use

model = NerDLModel.pretrained("ner_clinical_large","en","clinical/models")\
val model = NerDLModel.pretrained("ner_clinical_large","en","clinical/models")

Model Information

Name: ner_clinical_large  
Type: NerDLModel  
Compatibility: Spark NLP 2.5.0+  
License: Licensed  
Edition: Official  
Input labels: [sentence, token, word_embeddings]  
Output labels: [ner]  
Language: en  
Case sensitive: False  
Dependencies: embeddings_clinical  

Data Source

Trained on i2b2 augmented data with clinical_embeddings