Detect Neoplasms


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. Neoplasms NER is a Named Entity Recognition model that annotates text to find references to tumors. The only entity it annotates is MalignantNeoplasm. Neoplasms NER is trained with the ‘embeddings_scielowiki_300d’ word embeddings model, so be sure to use the same embeddings in the pipeline.

Predicted Entities



How to use

model = NerDLModel.pretrained("ner_neoplasms","es","clinical/models")\
val model = NerDLModel.pretrained("ner_neoplasms","es","clinical/models")

Model Information

Name: ner_neoplasms  
Type: NerDLModel  
Compatibility: Spark NLP 2.5.3+  
License: Licensed  
Edition: Official  
Input labels: [sentence, token, word_embeddings]  
Output labels: [ner]  
Language: es  
Case sensitive: False  
Dependencies: embeddings_scielowiki_300d  

Data Source

Named Entity Recognition model for Neoplasic Morphology