Adverse drug reaction classification with deep neural networks

Trung Huynh, Yulan He, Allistair Willis, Stefan Rueger

Research output: Chapter in Book/Published conference outputConference publication


We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.
Original languageEnglish
Title of host publicationProceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Number of pages11
Publication statusPublished - 16 Dec 2016
Event26th International Conference on Computational Linguistics: COLIN 2016 - Osaka, Japan
Duration: 11 Dec 201616 Dec 2016
Conference number: 26


Conference26th International Conference on Computational Linguistics
Abbreviated titleCOLING 2016

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 International Licence

Funding: EPSRC AMR4AMR project EP/M02735X/1).


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