Abstract
In recent years, adverse drug reactions have drawn more and more attention from the public, which may lead to great damage to the public health and cause massive economic losses to our society. As a result, it becomes a great challenge to detect the potential adverse drug reactions before and after putting drugs into the market. With the development of the Internet, health-related social networks have accumulated large amounts of users’ comments on drugs, which may contribute to detect the adverse drug reactions. To this end, we propose a novel framework to detect potential adverse drug reactions based on health-related social networks. In our framework, we first extract mentions of diseases and adverse drug reactions from users’ comments using conditional random fields with different levels of features, and then filter the indications of drugs and known adverse drug reactions by external biomedical resources to obtain the potential adverse drug reactions. On the basis, we propose a modified Skip-gram model to discover associated proteins of potential adverse drug reactions, which will facilitate the biomedical experts to determine the authenticity of the potential adverse reactions. Extensive experiments based on DailyStrength show that our framework is effective for detecting potential adverse drug reactions from users’ comments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Giacomini, K.M., Krauss, R.M., Roden, D.M., Eichelbaum, M., Hayden, M.R., Nakamura, Y.: When good drugs go bad. Nature 446(7139), 975–977 (2007)
Leaman, R., Wojtulewicz, L., Sullivan, R., Skariah, A., Yang, J., Gonzalez, G.: Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks. In: Paper Presented at the Proceedings of the 2010 Workshop on Biomedical Natural Language Processing (2010)
Rahmani, H., Weiss, G., Méndez-Lucio, O., Bender, A.: ARWAR: a network approach for predicting adverse drug reactions. Comput. Biol. Med. 68, 101–108 (2016)
Casillas, A., Pérez, A., Oronoz, M., Gojenola, K., Santiso, S.: Learning to extract adverse drug reaction events from electronic health records in Spanish. Expert Syst. Appl. 61, 235–245 (2016)
Dai, H.J., Touray, M., Jonnagaddala, J., Syed-Abdul, S.: Feature engineering for recognizing adverse drug reactions from Twitter posts. Information 7(2), 27 (2016)
Yates, A., Goharian, N.: ADRTrace: detecting expected and unexpected adverse drug reactions from user reviews on social media sites. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 816–819. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36973-5_92
Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Paper presented at the Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1 (2003)
Kuhn, M., Campillos, M., Letunic, I., Jensen, L.J., Bork, P.: A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6(1), 343 (2010)
Wishart, D.S., Knox, C., Guo, A.C., Shrivastava, S., Hassanali, M., Stothard, P., Woolsey, J.: DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34(suppl 1), D668–D672 (2006)
Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Hassanali, M.: DrugBank: a knowledgebase for drugs, drug reactions and drug targets. Nucleic Acids Res. 36(suppl 1), D901–D906 (2008)
Rindflesch, T.C., Fiszman, M.: The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J. Biomed. Inf. 36(6), 462–477 (2003)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Paper presented at the Advances in Neural Information Processing Systems (2013)
Gingrich, J.A.: Mutational analysis of the serotonergic system: recent findings using knockout mice. Curr. Drug Targets-CNS Neurol. Dis. 1(5), 449–465 (2002)
Goldman, D., Oroszi, G., Omalley, S., et al.: COMBINE genetics study: the pharmacogenetics of alcoholism treatment response: genes and mechanisms. J. Stud. Alcohol Suppl. 66(15), 56–64 (2005). discussion 33
Shishkina, G., Kalinina, T., Dygalo, N.: Attenuation of α 2A-adrenergic receptor expression in neonatal rat brain by RNA interference or antisense oligonucleotide reduced anxiety in adulthood. Neuroscience 129(3), 521–528 (2004)
Acknowledgements
This work is partially supported by grant from the Natural Science Foundation of China (Nos. 61277370, 61402075, 61572102, 61632011, 61602078, 61572098), Natural Science Foundation of Liaoning Province, China (Nos. 201202031, 2014020003), State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002), the Fundamental Research Funds for the Central Universities. The 12th five year national science and technology supporting programs of China under Grant No. 2015BAF20B02.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Xu, B., Lin, H., Zhao, M., Yang, Z., Wang, J., Zhang, S. (2016). Detecting Potential Adverse Drug Reactions from Health-Related Social Networks. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-50496-4_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50495-7
Online ISBN: 978-3-319-50496-4
eBook Packages: Computer ScienceComputer Science (R0)