Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks

ABSTRACT

Adverse reactions to drugs are among the most common causes of death in industrialized nations. Expensive clinical trials are not sufficient to uncover all of the adverse reactions a drug may cause, necessitating systems for post-marketing surveillance, or pharmacovigilance. These systems have typically relied on voluntary reporting by health care professionals. However, self-reported patient data has become an increasingly important resource, with efforts such as MedWatch from the FDA allowing reports directly from the consumer. In this paper, we propose mining the relationships between drugs and adverse reactions as reported by the patients themselves in user comments to health-related websites. We evaluate our system on a manually annotated set of user comments, with promising performance. We also report encouraging correlations between the frequency of adverse drug reactions found by our system in unlabeled data and the frequency of documented adverse drug reactions. We conclude that user comments pose a significant natural language processing challenge, but do contain useful extractable information which merits further exploration.

References

1. Alan R. Aronson. 2001. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In Proceedings of the AMIA Symposium, page 17. American Medical Informatics Association.
2. D. W. Bates, R. S. Evans, H. Murff, P. D. Stetson, L. Pizziferri, and G. Hripcsak. 2003. Detecting adverse events using information technology. Journal of the American Medical Informatics Association, 10(2):115--128.
3. A. Blenkinsopp, M. Wang, P. Wilkie, and P. A. Routledge. 2007. Patient reporting of suspected adverse drug reactions: a review of published literature and international experience. British Journal of Clinical Pharmacology, 63(2):148--156.
4. Rainer Burkard, Mauro Dell'Amico, and Silvano Martello. 2009. Assignment Problems. Society for Industrial and Applied Mathematics.
5. Jacob Cohen. 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1):37--46.
6. Nigel Collier, Son Doan, Ai Kawazoe, Reiko Matsuda Goodwin, Mike Conway, Yoshio Tateno, Quoc-Hung Ngo, Dinh Dien, Asanee Kawtrakul, Koichi Takeuchi, Mika Shigematsu, and Kiyosu Taniguchi. 2008. BioCaster: detecting public health rumors with a Web-based text mining system. Bioinformatics, 24(24):2940--2941.
7. comScore Media Metrix Canada. 2007. Key Measures Report - Health.
8. K. P. Davison, J. W. Pennebaker, and S. S. Dickerson. 2000. Who talks? The social psychology of illness support groups. The American Psychologist, 55(2):205--217.
9. K. M. Giacomini, R. M. Krauss, D. M. Roden, M. Eichelbaum, M. R. Hayden, and Y. Nakamura. 2007. When good drugs go bad. Nature, 446(7139):975--977.
10. Henk Harkema, John N. Dowling, Tyler Thornblade, and Wendy W. Chapman. 2009. ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports. Journal of Biomedical Informatics, 42(5):839851.
11. Mark Hepple. 2000. Independence and commitment: Assumptions for rapid training and execution of rule-based POS taggers. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pages 277--278.
12. Dyfrig A. Hughes, Adrian Bagust, Alan Haycox, and Tom Walley. 2001. The impact of non-compliance on the cost-effectiveness of pharmaceuticals: a review of the literature. Health Economics, 10(7):601--615.
13. International Society Of Drug Bulletins. 2005. Berlin Declaration on Pharmacovigilance.
14. Michael Kuhn, Monica Campillos, Ivica Letunic, Lars Juhl Jensen, and Peer Bork. 2010. A side effect resource to capture phenotypic effects of drugs. Molecular Systems Biology, 6:343--348.
15. Anne Lee, editor. 2006. Adverse Drug Reactions. Pharmaceutical Press, second edition.
16. Roberto Leone, Laura Sottosanti, Maria Luisa Iorio, Carmela Santuccio, Anita Conforti, Vilma Sabatini, Ugo Moretti, and Mauro Venegoni. 2008. Drug-Related Deaths: An Analysis of the Italian Spontaneous Reporting Database. Drug Safety, 31(8):703--713.
17. Charles Medawara, Andrew Herxheimer, Andrew Bell, and Shelley Jofre. 2002. Paroxetine, Panorama and user reporting of ADRs: Consumer intelligence matters in clinical practice and post-marketing drug surveillance. The International Journal of Risk and Safety in Medicine, 15(3):161169.
18. S. T. Moturu, H. Liu, and W. G. Johnson. 2008. Trust evaluation in health information on the World Wide Web. In 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 1525--1528.
19. National Library of Medicine. 2008. UMLS Knowledge Sources.
20. John Urquhart. 1999. Pharmacoeconomic consequences of variable patient compliance with prescribed drug regimens. PharmacoEconomics, 15(3):217--228.
21. Cornelis S. van Der Hooft, Miriam C. J. M. Sturkenboom, Kees van Grootheest, Herre J. Kingma, and Bruno H. Ch. Stricker. 2006. Adverse drug reaction-related hospitalisations: a nationwide study in The Netherlands. Drug Safety, 29(2):161--168.
22. William E. Winkler. 1999. The state of record linkage and current research problems.
23. World Health Organization. 1966. International Drug Monitoring: The Role of the Hospital.