Abstract
As one of the most important medical field subjects, adverse drug reaction seriously affects the patient’s life, health, and safety. Although many methods have been proposed, there are still plenty of important adverse drug reactions unknown, due to the complexity of the detection process. Social media, such as medical forums and social networking services, collects a large amount of drug use information from patients, and so is important for adverse drug reaction mining. However, most of the existing studies only involved a single source of data. This study automatically crawls the information published by users of the MedHelp Medical Forum. Then combining it with disease-related user posts which obtained from Twitter. We combine different word embeddings and utilize a multi-channel convolutional neural network to deal with the challenge that encountered in data representation of multiple sources, and further identify text containing adverse drug reaction information. In particular, in this process, to enable the model to take advantage of the morphological and shape information of words, we use a convolutional channel to learn the features from character-level embeddings of words. The experiment results show that the proposed method improved the representation of words and thus effectively detects adverse drug reactions from text.
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References
Giacomini KM, Krauss RM, Roden DM, Eichelbaum M, Hayden MR, Nakamura Y (2007) When good drugs go bad. Nature 446(7139):975–977
Yang H, Yang CC (2013) Harnessing social media for drug–drug interactions detection. In: 2013 IEEE international conference on healthcare informatics (ICHI). IEEE, pp 22–29
Chan A, Yap KYL, Koh D, Low XH, Cheung YT (2011) Electronic database to detect drug–drug interactions between antidepressants and oral anticancer drugs from a cancer center in Singapore: implications to clinicians. Pharmacoepidemiol Drug Saf 20(9):939–947
Jiang K, Zheng Y (2013) Mining Twitter data for potential drug effects. In: International conference on advanced data mining and applications. Springer, Berlin, pp 434–443
Yang CC, Yang H, Jiang L, Zhang M (2012) Social media mining for drug safety signal detection. In: Proceedings of the 2012 international workshop on Smart health and wellbeing. ACM, New York, pp 33–40
Leaman R, Wojtulewicz L, Sullivan R, Skariah A, Yang J, Gonzalez G (2010) Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks. In: Proceedings of the 2010 workshop on biomedical natural language processing. Association for Computational Linguistics, pp 117–125
Nikfarjam A, Sarker A, O’connor K, Ginn R, Gonzalez G (2015) Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc 22(3):671–681
Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882
Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. In: Advances in neural information processing systems, vol 1, pp 649–657
Santos CD, Zadrozny B (2014) Learning character-level representations for part-of-speech tagging. In: Proceedings of the 31st international conference on machine learning (ICML-14), pp 1818–1826
Ruder S, Ghaffari P, Breslin JG (2016) Character-level and multi-channel convolutional neural networks for large-scale authorship attribution. arXiv preprint arXiv:1609.06686
Sarker A, Gonzalez G (2015) Portable automatic text classification for adverse drug reaction detection via multi-corpus training. J Biomed Inform 53:196–207
Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6(1):343
Zeng QT, Tse T (2006) Exploring and developing consumer health vocabularies. J Am Med Inform Assoc 13(1):24–29
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, vol 2, pp 3111–3119
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781
Hui K, Yates A, Berberich K, de Melo G (2018) Co-pacrr: a context-aware neural ir model for ad-hoc retrieval. In: Proceedings of the eleventh ACM international conference on web search and data mining. ACM, New York, pp 279–287
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807–814
Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
Acknowledgements
The authors acknowledge the National Natural Science Foundation of China (Grant: 61572102), the National Natural Science Foundation of China (Grant: 61632011), the National Natural Science Foundation of China (Grant: 61572098).
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Shen, C., Lin, H., Guo, K. et al. Detecting adverse drug reactions from social media based on multi-channel convolutional neural networks. Neural Comput & Applic 31, 4799–4808 (2019). https://doi.org/10.1007/s00521-018-3722-8
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DOI: https://doi.org/10.1007/s00521-018-3722-8