Abstract
Social media sites such as Twitter is a platform where users usually express their feelings, opinions, and experiences, e.g., users often share their experiences about medications including adverse drug reactions in their tweets. Mining and detecting this information on adverse drug reactions could be immensely beneficial for pharmaceutical companies, drug-safety authorities and medical practitioners. However, the automatic extraction of adverse drug reactions from tweets is a nontrivial task due to the short and informal nature of tweets. In this paper, we aim to detect adverse drug reaction mentions in tweets where we assume that there exists a cause-effect relationship between drug names and adverse drug reactions. We propose a causality driven neural network-based approach to detect adverse drug reactions in tweets. Our approach applies a multi-head self attention mechanism to learn word-to-word interactions. We show that when the causal features are combined with the word-level semantic features, our approach can outperform several state-of-the-art adverse drug reaction detection approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bollegala, D., Maskell, S., Sloane, R., Hajne, J., Pirmohamed, M.: Causality patterns for detecting adverse drug reactions from social media: text mining approach. JMIR Public Health Surveill. 4(2), e51 (2018)
Chowdhury, S., Zhang, C., Yu, P.S.: Multi-task pharmacovigilance mining from social media posts. In: WWW (2018)
Cocos, A., Fiks, A.G., Masino, A.J.: Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. JAMIA 24(4), 813–821 (2017)
Evans, S., Waller, P.C., Davis, S.: Use of proportional reporting ratios (PRRS) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol. Drug Saf. 10(6), 483–486 (2001)
Godin, F., Vandersmissen, B., De Neve, W., Van de Walle, R.: Multimedia lab \(@ \) acl wnut ner shared task: named entity recognition for Twitter microposts using distributed word representations. In: Proceedings of the Workshop on Noisy User-Generated Text, pp. 146–153 (2015)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)
Hinton, G., Srivastava, N., Swersky, K.: Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Neural networks for machine learning, Coursera lecture 6e (2012)
Huynh, T., He, Y., Willis, A., Rüger, S.: Adverse drug reaction classification with deep neural networks. In: COLING, pp. 877–887 (2016)
Ji, Y., et al.: A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance. IEEE Trans. Inf Technol. Biomed. 15(3), 428–437 (2011)
Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282–289 (2001)
LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)
Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., Gonzalez, G.: Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. JAMIA 22(3), 671–681 (2015)
Qin, X., Kakar, T., Wunnava, S., Rundensteiner, E.A., Cao, L.: Maras: signaling multi-drug adverse reactions. In: KDD, pp. 1615–1623 (2017)
Song, Q., Li, B., Xu, Y.: Research on adverse drug reaction recognitions based on conditional random field. In: International Conference on Business and Information Management, pp. 97–101 (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Yang, C.C., Jiang, L., Yang, H., Tang, X.: Detecting signals of adverse drug reactions from health consumer contributed content in social media. In: Proceedings of ACM SIGKDD Workshop on Health Informatics. ACM (2012)
Yang, C.C., Yang, H., Jiang, L., Zhang, M.: Social media mining for drug safety signal detection. In: International Workshop on Smart Health and Wellbeing, pp. 33–40 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kayesh, H., Islam, M.S., Wang, J. (2019). A Causality Driven Approach to Adverse Drug Reactions Detection in Tweets. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-35231-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35230-1
Online ISBN: 978-3-030-35231-8
eBook Packages: Computer ScienceComputer Science (R0)