Open AccessIdentifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting
Author(s) -
Xi Yang,
Jiang Bian,
Ruogu Fang,
Ragnhildur I. Bjarnadottir,
William R. Hogan,
Yonghui Wu
Publication year - 2019
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocz144
Subject(s) - artificial intelligence , computer science , relationship extraction , support vector machine , convolutional neural network , machine learning , named entity recognition , artificial neural network , gradient boosting , recurrent neural network , boosting (machine learning) , f1 score , random forest , relation (database) , natural language processing , information extraction , data mining , management , economics , task (project management)
To develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge.
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