ABSTRACT
Current Adverse Drug Events (ADE) surveillance systems are often associated with a sizable time lag before such events are published. Online social media such as Twitter could describe adverse drug events in real-time, prior to official reporting. Deep learning has significantly improved text classification performance in recent years and can potentially enhance ADE classification in tweets. However, these models typically require large corpora with human expert-derived labels, and such resources are very expensive to generate and are hardly available. Semi-supervised deep learning models, which offer a plausible alternative to fully supervised models, involve the use of a small set of labeled data and a relatively larger collection of unlabeled data for training. Traditionally, these models are trained on labeled and unlabeled data from similar topics or domains. In reality, millions of tweets generated daily often focus on disparate topics, and this could present a challenge for building deep learning models for ADE classification with random Twitter stream as unlabeled training data. In this work, we build several semi-supervised convolutional neural network (CNN) models for ADE classification in tweets, specifically leveraging different types of unlabeled data in developing the models to address the problem. We demonstrate that, with the selective use of a variety of unlabeled data, our semi-supervised CNN models outperform a strong state-of-the-art supervised classification model by +9.9% F1-score. We evaluated our models on the Twitter data set used in the PSB 2016 Social Media Shared Task. Our results present the new state-of-the-art for this data set.
- E. Benzschawel. Identifying potential adverse drug events in tweets using bootstrapped lexicons. Master's thesis, Brandeis University, 5 2016.Google Scholar
- T. Berg-Kirkpatrick, D. Burkett, and D. Klein. An empirical investigation of statistical significance in nlp. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012. Google ScholarDigital Library
- O. Bodenreider. The unified medical language system (umls): integrating biomedical terminology. Nucleic acids research, 32:D267--D270, 2004.Google Scholar
- E. G. Brown, L. Wood, and S. Wood. The medical dictionary for regulatory activities (meddra). Drug Safety, 20(2):109--117, 2012.Google ScholarCross Ref
- H.-J. Dai, M. Touray, J. Jonnagaddala, and S. Syed-Abdul. Feature engineering for recognizing adverse drug reactions from twitter posts. Information, 7(2):27, 2016.Google ScholarCross Ref
- C. N. dos Santos and M. Gatti. Deep convolutional neural networks for sentiment analysis of short texts. In COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 2014.Google Scholar
- D. Egger, F. Uzdilli, M. Cieliebak, and L. Derczynski. Adverse drug reaction detection using an adapted sentiment classifier. In Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing, 2016.Google Scholar
- C. C. Freifeld, J. S. Brownstein, C. M. Menone, W. Bao, R. Filice, T. Kass-Hout, and N. Dasgupta. Digital drug safety surveillance: Monitoring pharmaceutical products in twitter. Drug Safety, 37(5):343--350, 2014.Google ScholarCross Ref
- H. Gurulingappa, A. M. Rajput, A. Roberts, J. Fluck, M. Hofmann-Apitius, and L. Toldo. Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. Journal of Biomedical Informatics, pages 885--892, 2012. Google ScholarDigital Library
- S. A. Hasan, Y. Ling, J. Liu, and O. Farri. Exploiting neural embeddings for social media data analysis. In Proceedings of The Twenty-Fourth Text REtrieval Conference, TREC 2015, Gaithersburg, Maryland, USA, November 17-20, 2015, 2015.Google Scholar
- R. Johnson and T. Zhang. Semi-supervised convolutional neural networks for text categorization via region embedding. In Proceedings of the 29th Annual Conference on Advances in Neural Information Processing Systems (NIPS), 2015. Google ScholarDigital Library
- R. Johnson and T. Zhang. Supervised and semi-supervised text categorization using lstm for region embeddings. arXiv preprint arXiv:1602.02373, 2016.Google Scholar
- J. Jonnagaddala, T. R. Jue, and H. Dai. Binary classification of twitter posts for adverse drug reactions. In Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing, Big Island, HI, USA, 2016.Google Scholar
- A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759, 2016.Google Scholar
- N. Kalchbrenner, E. Grefenstette, and P. Blunsom. A convolutional neural network for modelling sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014.Google ScholarCross Ref
- N. Kang, B. Singh, C. Bui, Z. Afzal, E. M. van Mulligen, and J. A. Kors. Knowledge-based extraction of adverse drug events from biomedical text. BMC bioinformatics, 15(1):1, 2014.Google ScholarCross Ref
- S. Karimi, A. Metke-Jimenez, M. Kemp, and C. Wang. Cadec: A corpus of adverse drug event annotations. Journal of Biomedical Informatics, 55:73--81, 2015. Google ScholarDigital Library
- Y. Kim. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014.Google ScholarCross Ref
- J. Lardon, R. Abdellaoui, F. Bellet, H. Asfari, J. Souvignet, N. Texier, M. C. Jaulent, M. N. Beyens, A. Burgun, and C. Bousquet. Adverse drug reaction identification and extraction in social media: A scoping review. Journal of Medical Internet Research, 17(7):e171, 2015.Google ScholarCross Ref
- R. Leaman, R. I. Dogan, and Z. Lu. Dnorm: disease name normalization with pairwise learning to rank. Bioinformatics, 29(22):2909--2917, 2013.Google ScholarCross Ref
- R. Leaman, R. Khare, and Z. Lu. Challenges in clinical natural language processing for automated disorder normalization. Journal of Biomedical Informatics, 57:28--37, 2015. Google ScholarDigital Library
- Y. Lecun and Y. Bengio. Convolutional Networks for Images, Speech and Time Series. The MIT Press, 1995.Google Scholar
- J. Y. Lee and F. Dernoncourt. Sequential short-text classification with recurrent and convolutional neural networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.Google ScholarCross Ref
- K. Lee, A. Agrawal, and A. Choudhary. Real-time disease surveillance using twitter data: Demonstration on flu and cancer. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13, pages 1474--1477, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- K. Lee, A. Agrawal, and A. Choudhary. Mining social media streams to improve public health allergy surveillance. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 815--822, Aug 2015. Google ScholarDigital Library
- K. Lee, D. Palsetia, R. Narayanan, M. M. A. Patwary, A. Agrawal, and A. Choudhary. Twitter trending topic classification. In 2011 IEEE 11th International Conference on Data Mining Workshops, pages 251--258, Dec 2011. Google ScholarDigital Library
- N. Limsopatham and N. Collier. Normalising medical concepts in social media texts by learning semantic representation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers, 2016.Google ScholarCross Ref
- A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. Learning word vectors for sentiment analysis. In ACL, 2011. Google ScholarDigital Library
- T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, 2013. Google ScholarDigital Library
- G. A. Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39--41, 1995. Google ScholarDigital Library
- A. Nikfarjam, A. Sarker, K. O'Connor, R. Ginn, and G. Gonzalez. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22:671--681, 2015.Google ScholarCross Ref
- B. Ofoghi, S. Siddiqui, and K. Verspoor. Read-biomed-ss: Adverse drug reaction classification of microblogs using emotional and conceptual enrichment. In Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing, 2016.Google Scholar
- V. Plachouras, J. L. Leidner, and A. G. Garrow. Quantifying self-reported adverse drug events on twitter: Signal and topic analysis. In Proceedings of the 7th 2016 International Conference on Social Media & Society, 2016. Google ScholarDigital Library
- M. Rastegar-Mojarad, R. K. Elayavilli, Y. Yu, and H. Liu. Detecting signals in noisy data-can ensemble classifiers help identify adverse drug reaction in tweets. In Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing, 2016.Google Scholar
- H. Sampathkumar, X. Chen, and B. Luo. Mining adverse drug reactions from online healthcare forums using hidden markov model. BMC Medical Informatics and Decision Making, 14(1):1--18, 2014.Google ScholarCross Ref
- A. Sarker, R. E. Ginn, A. Nikfarjam, K. O'Connor, K. Smith, S. Jayaraman, T. Upadhaya, and G. Gonzalez. Utilizing social media data for pharmacovigilance: A review. Journal of Biomedical Informatics, 54:202--212, 2015. Google ScholarDigital Library
- A. Sarker and G. Gonzalez. Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of Biomedical Informatics, 53:196--207, 2015. Google ScholarDigital Library
- D. Tang, B. Qin, and T. Liu. Document modeling with gated recurrent neural network for sentiment classification. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015.Google ScholarCross Ref
- Unified medical language systemMakeUppercase umls metathesaurus. https://www.nlm.nih.gov/pubs/factsheets/umlsmeta.html. {accessed September-2016}.Google Scholar
- S. J. Yeleswarapu, A. Rao, T. Joseph, V. Saipradeep, and R. Srinivasan. A pipeline to extract drug-adverse event. BMC Med. Inf. & Decision Making, 14:13, 2014.Google ScholarCross Ref
- X. Zhang, J. Zhao, and Y. LeCun. Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, 2015. Google ScholarDigital Library
- Z. Zhang, J.-Y. Nie, and X. Zhang. An ensemble method for binary classification of adverse drug reactions from social media. Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing, 2016.Google Scholar
Index Terms
- Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks
Recommendations
Semi- and Weakly- Supervised Semantic Segmentation with Deep Convolutional Neural Networks
MM '15: Proceedings of the 23rd ACM international conference on MultimediaSuccessful semantic segmentation methods typically rely on the training datasets containing a large number of pixel-wise labeled images. To alleviate the dependence on such a fully annotated training dataset, in this paper, we propose a semi- and weakly-...
Semi-supervised text classification with deep convolutional neural network using feature fusion approach
WI '19: IEEE/WIC/ACM International Conference on Web IntelligenceSupervised learning algorithms employ labeled training data for classification purposes while obtaining labeled data for large datasets is costly and time consuming. Semi-supervised learning algorithms, on the contrary, use a small set of labeled data ...
A robust semi-supervised classification method for transfer learning
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge managementThe transfer learning problem of designing good classifiers with a high generalization ability by using labeled samples whose distribution is different from that of test samples is an important and challenging research issue in the fields of machine ...
Comments