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Adverse Drug Reaction Classification With Deep Neural Networks

Huynh, Trung; He, Yulan; Willis, Alistair and Rüger, Stefan (2016). Adverse Drug Reaction Classification With Deep Neural Networks. In: Proceedings of COLING 2016: Technical Papers, COLING, pp. 877–887.

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Abstract

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.

Item Type: Conference or Workshop Item
Copyright Holders: 2016 The Author
ISBN: 4-87974-702-5, 978-4-87974-702-0
Extra Information: This work is licenced under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/
Keywords: Convolutional Recurrent Neural Network, Convolutional Neural Network with Attention, adverse drug reactions, binary classification, natural language processing
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 48109
Depositing User: Stefan Rüger
Date Deposited: 13 Jan 2017 15:05
Last Modified: 14 Jan 2017 01:04
URI: http://oro.open.ac.uk/id/eprint/48109
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