Posology Model

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 posology, this NER is trained with the ‘embeddings_clinical’ word embeddings model, so be sure to use the same embeddings in the pipeline

Predicted Entities

DOSAGE, DRUG, DURATION, FORM, FREQUENCY, ROUTE,STRENGTH

Live Demo Open in ColabDownload

How to use

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

Model Information

Name: ner_posology  
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 the 2018 i2b2 dataset and FDA Drug datasets with embeddings_clinical. https://open.fda.gov/