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Publication . Conference object . Part of book or chapter of book . 2016

Adverse drug reaction classification with deep neural networks

Huynh, Trung; He, Yulan; Willis, Alistair; Rüger, Stefan;
Open Access
Published: 16 Dec 2016
Country: United Kingdom

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.

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41 references, page 1 of 5

[Alex Krizhevsky et al.2012] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. Advances in neural information processing systems (NIPS), pages 1097-1105.

[Aramaki et al.2010] Eiji Aramaki, Yasuhide Miura, Masatsugu Tonoike, Tomoko Ohkuma, Hiroshi Masuichi, Kayo Waki, and Kazuhiko Ohe. 2010. Extraction of adverse drug effects from clinical records. Studies in Health Technology and Informatics, 160 (PART 1):739-743.

[Bahdanau et al.2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In 5th International Conference on Learning Representations (ICLR), pages 1-15.

[Benton et al.2011] Adrian Benton, Lyle Ungar, Shawndra Hill, Sean Hennessy, Jun Mao, Annie Chung, Charles E. Leonard, and John H. Holmes. 2011. Identifying potential adverse effects using the web: A new approach to medical hypothesis generation. Journal of Biomedical Informatics, 44(6):989-996. [OpenAIRE]

[Cho et al.2014] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Dzmitry Bahdanau, Holger Schwenk, Yoshua Bengio, Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Empiricial Methods in Natural Language Processing (EMNLP).

[Collobert et al.2011] Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research, 12:2493-2537.

[Friedman2009] Carol Friedman. 2009. Discovering Novel Adverse Drug Events Using Natural Language Processing and Mining of the Electronic Health Record. In 12th Conference on Artificial Intelligence in Medicine (AIME), pages 1-5.

[Ginn et al.2014] Rachel Ginn, Pranoti Pimpalkhute, Azadeh Nikfarjam, Apur Patki, Karen Oconnor, Abeed Sarker, Karen Smith, and Graciela Gonzalez. 2014. Mining Twitter for Adverse Drug Reaction Mentions: A Corpus and Classification Benchmark. In proceedings of the 4th Workshop on Building and Evaluating Resources for Health and Biomedical Text Processing (BioTxtM).

[Glorot et al.2011] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep Sparse Rectifier Neural Networks. In 14th International Conference on Artificial Intelligence and Statistics (AISTATS), pages 315-323.

[Gurulingappa and Fluck2011] H Gurulingappa and J Fluck. 2011. Identification of adverse drug event assertive sentences in medical case reports. In 1st international workshop on knowledge discovery and health care management (KD-HCM) co-located at the European conference on machine learning and principles and practice of knowledge discovery in databases (ECML PKDD), pages 16-27.

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