ABSTRACT
We present an end-to-end text mining methodology for relation extraction of adverse drug reactions (ADRs) from medical forums on the Web. Our methodology is novel in that it combines three major characteristics: (i) an underlying concept of using a head-driven phrase structure grammar (HPSG) based parser; (ii) domain-specific relation patterns, the acquisition of which is done primarily using unsupervised methods applied to a large, unlabeled text corpus; and (iii) automated post-processing algorithms for enhancing the set of extracted relations. We empirically demonstrate the ability of our proposed approach to predict ADRs prior to their reporting by the Food and Drug Administration (FDA). Put differently, we put our approach to a predictive test by demonstrating that our methodology can credibly point to ADRs that were not uncovered in clinical trials for evaluating new drugs that come to market but were only reported later on by the FDA as a label change.
Supplemental Material
- M. Banko, O. Etzioni, and T. Center. The tradeoffs between open and traditional relation extraction. In ACL, volume 8, pages 28--36. Citeseer, 2008.Google Scholar
- A. Benton, L. Ungar, S. Hill, S. Hennessy, J. Mao, A. Chung, C. E. Leonard, and J. H. Holmes. Identifying potential adverse effects using the web: A new approach to medical hypothesis generation. Journal of Biomedical Informatics, 44(6):989--996, 2011. Google ScholarDigital Library
- B. W. Chee, R. Berlin, and B. Schatz. Predicting adverse drug events from personal health messages. 2011:217--226, 2011.Google Scholar
- K. W. Church and P. Hanks. Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1):22--29, 1990. Google ScholarDigital Library
- R. Feldman, M. Fresko, J. Goldenberg, O. Netzer, and L. H. Ungar. Extracting product comparisons from discussion boards. In International Conference on Data Mining (ICDM), pages 469--474, 2007. Google ScholarDigital Library
- R. Feldman, M. Fresko, J. Goldenberg, O. Netzer, and L. H. Ungar. Using text mining to analyze user forums. In International Conference on Service Systems and Service Management (ICSSSM), pages 1--5, 2008.Google ScholarCross Ref
- N. Glance, M. Hurst, K. Nigam, M. Siegler, R. Stockton, and T. Tomokiyo. Analyzing online discussion for marketing intelligence. In Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, pages 1172--1173, 2005. Google ScholarDigital Library
- N. Glance, M. Hurst, K. Nigam, M. Siegler, R. Stockton, and T. Tomokiyo. Deriving marketing intelligence from online discussion. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pages 419--428, 2005. Google ScholarDigital Library
- S. Hariharan and T. Ramkumar. Mining product reviews in web forums. In Information Retrieval Methods for Multidisciplinary Applications, pages 78--94. IGI Global, 2013.Google ScholarCross Ref
- R. Leaman, L. Wojtulewicz, R. Sullivan, A. Skariah, J. Yang, and G. Gonzalez. 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, pages 117--125, 2010. Google ScholarDigital Library
- T. Y. Lee and E. T. Bradlow. Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5):881--894, 2011.Google ScholarCross Ref
- X. Liu and H. Chen. Azdrugminer: An information extraction system for mining patient-reported adverse drug events in online patient forums. In Smart Health, pages 134--150. Springer, 2013. Google ScholarDigital Library
- O. Netzer, R. Feldman, J. Goldenberg, and M. Fresko. Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3):521--543, 2012. Google ScholarDigital Library
- A. Nikfarjam and G. H. Gonzalez. Pattern mining for extraction of mentions of adverse drug reactions from user comments. In American Medical Informatics Association (AMIA) Annual Symposium Proceedings 2011, pages 1019--1026. American Medical Informatics Association, 2011.Google Scholar
- A. Patki, A. Sarker, P. Pimpalkhute, A. Nikfarjam, R. Ginn, K. O'Connor, K. Smith, and G. Gonzalez. Mining adverse drug reaction signals from social media: Going beyond extraction. In Proceedings of the 22nd Annual International Conference on Intelligent Systems for Molecular Biology (ISBM), pages 9--16, 2014.Google Scholar
- B. Rozenfeld and R. Feldman. Unsupervised lexicon acquisition for hpsg-based relation extraction. In International Joint Conference on Artificial Intelligence (IJCAI), pages 1890--1895, 2011. Google ScholarDigital Library
- I. Segura-Bedmar, S. de la Pena, and P. Martínez. Extracting drug indications and adverse drug reactions from spanish health social media. In Proceedings of the 2014 Workshop on Biomedical Natural Language Processing, pages 98--106, 2014.Google Scholar
- P. D. Turney and M. L. Littman. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4):315--346, 2003. Google ScholarDigital Library
- S. Wang, H. Lin, D. Ferguson, and C. Zhai. Sideeffectptm: An unsupervised topic model to mine adverse drug reactions from health forums. 2014.Google ScholarDigital Library
- R. W. White, N. P. Tatonetti, N. H. Shah, R. B. Altman, and E. Horvitz. Web-scale pharmacovigilance: Listening to signals from the crowd. Journal of the American Medical Informatics Association (JAMIA), 20(3):404--408, 2013.Google Scholar
- C. C. Yang, L. Jiang, H. Yang, and X. Tang. Detecting signals of adverse drug reactions from health consumer contributed content in social media. In Proceedings of ACM SIGKDD Workshop on Health Informatics, 2012.Google Scholar
- C. C. Yang, H. Yang, L. Jiang, and M. Zhang. Social media mining for drug safety signal detection. In Proceedings of the 2012 International Workshop on Smart Health and Wellbeing (SHB), pages 33--40, 2012. Google ScholarDigital Library
Index Terms
- Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions
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