Ner DL Model Risk Factors

Description

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. Pretrained named entity recognition deep learning model for Heart Disease Risk Factors and Personal Health Information.

Predicted Entities

CAD, DIABETES, FAMILY_HIST, HYPERLIPIDEMIA, HYPERTENSION, MEDICATION, OBESE, PHI, SMOKER

Live Demo Open in ColabDownload

How to use

model = NerDLModel.pretrained("ner_risk_factors","en","clinical/models")\
	.setInputCols("sentence","token","word_embeddings")\
	.setOutputCol("ner")
val model = NerDLModel.pretrained("ner_risk_factors","en","clinical/models")
	.setInputCols("sentence","token","word_embeddings")
	.setOutputCol("ner")

Model Information

Name: ner_risk_factors  
Type: NerDLModel  
Compatibility: Spark NLP 2.4.2+  
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 plain n2c2 2014: De-identification and Heart Disease Risk Factors Challenge datasets with embeddings_clinical https://portal.dbmi.hms.harvard.edu/projects/n2c2-2014/