Abstract
Drug safety surveillance plays a significant role in supporting medication decision-making by both healthcare providers and patients. Extracting adverse drug events (ADEs) from social media provides a promising direction to addressing this challenging task. Prior studies typically perform lexicon-based extraction using existing dictionaries or medical lexicons. While those approaches can capture ADEs and identify risky drugs from patient social media postings, they often fail to detect those ADEs whose descriptive words do not exist in medical lexicons and dictionaries. In addition, their performance is inferior when ADE related social media content is expressed in an ambiguous manner. In this research, we propose a research framework using advanced natural language processing and deep learning for high-performance ADE extraction. The framework consists of training the word embeddings using a large medical domain corpus to capture precise semantic and syntactic word relationships, and a deep learning based named entity recognition method for drug and ADE entity identification and prediction. Experimental results show that our framework significantly outperforms existing models when extracting ADEs from social media in different test beds.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Harpaz, R., DuMouchel, W., Shah, N.H., Madigan, D., Ryan, P., Friedman, C.: Novel data-mining methodologies for adverse drug event discovery and analysis. Clin. Pharmacol. Ther. 91, 1010–1021 (2012). doi:10.1038/clpt.2012.50
Hauben, M., Bate, A.: Decision support methods for the detection of adverse events in post-marketing data. Drug Discov. Today 14, 343–357 (2009). doi:10.1016/j.drudis.2008.12.012
World Health Organization. The importance of pharmacovigilance (2012)
Bate, A., Evan, S.: Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol. Drug Saf. 18, 427–436 (2009). doi:10.1002/pds.1742
Miller, A.R., Tucker, C.: Active social media management: the case of health care. Inform. Syst. Res. 24, 52–70 (2013). doi:10.2139/ssrn.1984973
Basch, E.: The missing voice of patients in drug-safety reporting. N. Engl. J. Med. 362, 865–869 (2010). doi:10.1056/NEJMp0911494
Liu, X., Chen, H.: A research framework for pharmacovigilance in health social media: identification and evaluation of patient adverse drug event reports. J. Biomed. Inform. 58, 268–279 (2015). doi:10.1016/j.jbi.2015.10.011
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: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics (2010)
Nikfarjam, A., Gonzalez, G.H.: Pattern mining for extraction of mentions of adverse drug reactions from user comments. In: AMIA Annual Symposium Proceedings (2011)
Benton, A., Ungar, L., Hill, S., Hennessy, S., Mao, J., Chung, A., Leonard, C.E., Holmes, J.H.: Identifying potential adverse effects using the web: a new approach to medical hypothesis generation. J. Biomed. Inform. 44, 989–996 (2011). doi:10.1016/j.jbi.2011.07.005
Wu, H., Fang, H., Stanhope, S.J.: Exploiting online discussions to discover unrecognized drug side effects. Methods Inf. Med. 52, 152–159 (2013). doi:10.3414/ME12-02-0004
Bian, J., Topaloglu, U., Yu, F.: Towards large-scale twitter mining for drug-related adverse events. In: Proceedings of the 2012 International Workshop on Smart Health and Wellbeing. ACM (2012)
Mikolov, T., Chen, K., Corrado, G.S., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv preprint arXiv:1301.3781
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (2013). arXiv:1310.4546
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011). arXiv:1103.0398
Goller, C., Kuchler, A.: Learning task-dependent distributed representations by backpropagation through structure. In: Neural Networks IEEE International Conference. IEEE (1996). doi:10.1109/ICNN.1996.548916
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12, 2451–2471 (2000). doi:10.1162/089976600300015015
Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2013). arXiv:1303.5778
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix. Tagging Protocol
Appendix. Tagging Protocol
Entity Types: describe whether the entity class each word belongs to
For each word in the sentence:
Drug entity – If the word describes the name of a drug
ADE entity – If the word describes the ADE of a drug
Others – If the word neither describes the name nor the ADE of a drug.
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xia, L., Wang, G.A., Fan, W. (2017). A Deep Learning Based Named Entity Recognition Approach for Adverse Drug Events Identification and Extraction in Health Social Media. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-67964-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67963-1
Online ISBN: 978-3-319-67964-8
eBook Packages: Computer ScienceComputer Science (R0)