Abstract
Post-marketing drug surveillance is a critical component of drug safety. Drug regulatory agencies such as the U.S. Food and Drug Administration (FDA) rely on voluntary reports from health professionals and consumers contributed to its FDA Adverse Event Reporting System (FAERS) to identify adverse drug events (ADEs). However, it is widely known that FAERS underestimates the prevalence of certain adverse events. Popular patient social media sites such as DailyStrength and PatientsLikeMe provide new information sources from which patient-reported ADEs may be extracted. In this study, we propose an analytical framework for extracting patient-reported adverse drug events from online patient forums. We develop a novel approach – the AZDrugMiner system – based on statistical learning to extract ad-verse drug events in patient discussions and identify reports from patient experiences. We evaluate our system using a set of manually annotated forum posts which show promising performance. We also examine correlations and differences between patient ADE reports extracted by our system and reports from FAERS. We conclude that patient social media ADE reports can be extracted effectively using our proposed framework. Those patient reports can reflect unique perspectives in treatment and be used to improve patient care and drug safety.
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References
Bunescu, R.C., Mooney, R.J.: A Shortest Path Dependency Kernel for Relation Extraction. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 724–731 (2005)
Benton, A., Ungar, L., Hill, S., Hennessy, S., Mao, J., Chung, A., Holmes, J.H.: Identifying potential adverse effects using the web: A new approach to medical hypothesis generation. Journal of Biomedical Informatics 44(6), 989–996 (2001)
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, pp. 25–32. ACM (2012)
Borgwardt, K.M., Kriegel, H.P.: Shortest-path kernels on graphs. In: Fifth IEEE International Conference on Data Mining, p. 8. IEEE (2005)
Chee, B.W., Berlin, R., Schatz, B.: Predicting adverse drug events from personal health messages. In: AMIA Annual Symposium Proceedings, vol. 2011, pp. 217–226 (2011)
Consumer Health Vocabulary, http://www.consumerhealthvocab.org/
Chapman, W., Hilert, D., Velupillai, S., Kvist, M., et al.: Extending the NegEx Lexicon for Multiple Languages. In: Proceedings of the 14th World Congress on Medical & Health Informatics (2013)
DailyStrength, http://www.dailystrength.org/
FDA’s Adverse Drug Event Reporting System (FAERS), http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/default.htm
Jiang, Y.L., Liao, Q.V., Cheng, Q., Berlin, R.B., Schartz, B.R.: Designing and evaluating a clustering system for organizing and integrating patient drug outcomes in personal health messages. In: AMIA Annual Symposium Proceedings 2012, pp. 417–426 (2012)
Joachims, T.: Transductive inference for text classification using support vector machines. In: Machine Learning- International Workshop, pp. 200–209 (1999)
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, 34 (2010)
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, pp. 117–125. ACL (2010)
Li, S., Huang, C.R., Zhou, G., Lee, S.Y.M.: Employing personal/impersonal views in supervised and semi-supervised sentiment classification. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 414–423 (2010)
Li, J., Zhang, Z., Li, X., Chen, H.: Kernel-based Learning for Biomedical Relation Extraction. Journal of the American Society for Information Sciences and Technology 59(5), 756–769 (2008)
MedEffect, http://www.hc-sc.gc.ca/dhp-mps/medeff/index-eng.php
MetaMap, http://metamap.nlm.nih.gov/
Nikfarjam, A., Gonzalez, G.H.: Pattern mining for extraction of mentions of Adverse Drug Reaction from user comments. In: Proceeding of 2011 AMIA Annual Symposium, pp. 1019–1026 (2011)
OpenNLP, http://opennlp.apache.org/
PatientsLikeMe, http://www.patientslikeme.com/
Stanford CoreNLP, http://www-nlp.stanford.edu/software/dependencies_manual.pdf
SVM-Light, http://svmlight.joachims.org/
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. Journal of Machine Learning Research (2000)
Unified Medical Language System (UMLS), http://www.nlm.nih.gov/research/umls/
Yang, C.C., Yang, H., Jiang, L., Zhang, M.: Social media mining for drug safety signal detection. In: Proceedings of the 2012 International Workshop on Smart Health and Wellbeing, pp. 33–40. ACM (2012)
Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. Journal of Machine Learning Research 3, 1083–1106 (2003)
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Liu, X., Chen, H. (2013). AZDrugMiner: An Information Extraction System for Mining Patient-Reported Adverse Drug Events in Online Patient Forums. In: Zeng, D., et al. Smart Health. ICSH 2013. Lecture Notes in Computer Science, vol 8040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39844-5_16
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DOI: https://doi.org/10.1007/978-3-642-39844-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39843-8
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