Harpaz R, Callahan A, Tamang S, Low Y, Odgers D, Finlayson S, Jung K, LePendu P, Shah NH. Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf. 2014;37(10):777–90.
CAS
Article
Google Scholar
Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res. 2013;15(4):e85.
Article
Google Scholar
Pew Research Center: Internet ST. Health Online 2013 [Internet]. Pew Research Center; 2015. https://www.pewinternet.org/2013/01/15/health-online-2013/. Accessed 6 Sept 2018.
Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K, Jayaraman S, Upadhaya T, Gonzalez G. Utilizing social media data for pharmacovigilance: a review. J Biomed Inform. 2015;1(54):202–12.
Article
Google Scholar
Freifeld CC, Brownstein JS, Menone CM, Bao W, Filice R, Kass-Hout T, Dasgupta N. Digital drug safety surveillance: monitoring pharmaceutical products in twitter. Drug Saf. 2014;37(5):343–50.
CAS
Article
Google Scholar
Alvaro N, Conway M, Doan S, Lofi C, Overington J, Collier N. Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug use. J Biomed Inform. 2015;58:280–7.
Article
Google Scholar
Patki A, Sarker A, Pimpalkhute P, Nikfarjam A, Ginn R, O’Connor K, et al. Mining adverse drug reaction signals from social media: going beyond extraction. Proc BioLinkSig. 2014;2014:1–8.
Google Scholar
Rastegar-Mojarad M, Elayavilli RK, Yu Y, Liu H. 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.
Nikfarjam A, Sarker A, O’connor K, Ginn R, Gonzalez G. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc. 2015;22(3):671–81.
PubMed
PubMed Central
Google Scholar
Karimi S, Metke-Jimenez A, Nguyen A. CADEminer: a system for mining consumer reports on adverse drug side effects. In: Proceedings of the eighth workshop on exploiting semantic annotations in information retrieval: 2015: ACM, 2015; p. 47–50.
Sarker A, Belousov M, Friedrichs J, Hakala K, Kiritchenko S, Mehryary F, Han S, Tran T, Rios A, Kavuluru R, de Bruijn B. Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task. J Am Med Inform Assoc. 2018;25(10):1274–83.
Article
Google Scholar
Metke-Jimenez A, Karimi S. Concept extraction to identify adverse drug reactions in medical forums: a comparison of algorithms. arXiv preprint arXiv:150406936. 2015.
Tutubalina E, Nikolenko S. Combination of deep recurrent neural networks and conditional random fields for extracting adverse drug reactions from user reviews. J Healthc Eng. 2017;2017:1–9.
Article
Google Scholar
Liu J, Li A, Seneff S. Automatic drug side effect discovery from online patient-submitted reviews: focus on statin drugs. In: Proceedings of First international conference on advances in information mining and management (IMMM): Barcelona, Spain; 2011. p. 23–9.
Risson V, Saini D, Bonzani I, Huisman A, Olson M. Validation of social media analysis for outcomes research: identification of drivers of switches between oral and injectable therapies for multiple sclerosis. Value Health. 2015;18(7):A729.
Article
Google Scholar
Chee BW, Berlin R, Schatz B. Predicting adverse drug events from personal health messages. AMIA Annu Symp Proc. 2011;2011:217–26.
PubMed
PubMed Central
Google Scholar
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. USA: Association for Computational Linguistics; 2010. p. 117–25.
Powell GE, Seifert HA, Reblin T, Burstein PJ, Blowers J, Menius JA, et al. Social media listening for routine post-marketing safety surveillance. Drug Saf. 2016;39(5):443–54.
Article
Google Scholar
Freifeld CC. Digital Pharmacovigilance: the medwatcher system for monitoring adverse events through automated processing of internet social media and crowdsourcing. 2014. Doctoral dissertation, Boston University.
Nikfarjam A, Gonzalez GH. Pattern mining for extraction of mentions of Adverse Drug Reactions from user comments. AMIA Annu Symp Proc. 2011;2011:1019–26.
White RW, Harpaz R, Shah NH, DuMouchel W, Horvitz E. Toward enhanced pharmacovigilance using patient-generated data on the internet. Clin Pharmacol Ther. 2014;96(2):239.
CAS
Article
Google Scholar
Yang CC, Yang H, Jiang L, Zhang M. Social media mining for drug safety signal detection. Proceedings of the 2012 international workshop on smart health and wellbeing - SHB ’12 [Internet]. Maui, Hawaii, USA: ACM Press; 2012 p. 33. Available from: http://dl.acm.org/citation.cfm?doid=2389707.2389714.
Sampathkumar H, Chen X-W, Luo B. Mining adverse drug reactions from online healthcare forums using hidden Markov model. BMC Med Inform Decis Mak. 2014;14(1):1.
Article
Google Scholar
Chen X, Deldossi M, Aboukhamis R, Faviez C, Dahamna B, Karapetiantz P, et al. Mining adverse drug reactions in social media with named entity recognition and semantic methods. Stud Health Technol Inform. 2017;245:322–6.
PubMed
Google Scholar
Tricco AC, Zarin W, Lillie E, Jeblee S, Warren R, Khan PA, Robson R, Hirst G, Straus SE. Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review. BMC Med Inform Decis Mak. 2018;18(1):38.
Article
Google Scholar
Topaz M, Lai K, Dhopeshwarkar N, Seger DL, Sa’adon R, Goss F, et al. Clinicians’ reports in electronic health records versus patients’ concerns in social media: a pilot study of adverse drug reactions of aspirin and atorvastatin. Drug Saf. 2016;39(3):241–50.
CAS
Article
Google Scholar
Akay A, Dragomir A, Erlandsson B-E. Network-based modeling and intelligent data mining of social media for improving care. IEEE J Biomed Health Inform. 2015;19(1):210–8.
Article
Google Scholar
Dole O. Discovering drug side effects with crowdsourcing. 2013. https://www.crowdflower.com/discovering-drug-side-effects-with-crowedsourcing/. Accessed June 2019.
Pages A, Bondon-Guitton E, Montastruc JL, Bagheri H. Undesirable effects related to oral antineoplastic drugs: comparison between patients’ internet narratives and a national pharmacovigilance database. Drug Saf. 2014;37(8):629–37.
CAS
Article
Google Scholar
Caster O, Dietrich J, Kürzinger ML, Lerch M, Maskell S, Norén GN, Tcherny-Lessenot S, Vroman B, Wisniewski A, van Stekelenborg J. Assessment of the utility of social media for broad-ranging statistical signal detection in pharmacovigilance: results from the WEB-RADR project. Drug Saf. 2018;41(12):1355–69.
CAS
Article
Google Scholar
Meyboom RH, Egberts AC, Edwards IR, Hekster YA, de Koning FH, Gribnau FW. Principles of signal detection in pharmacovigilance. Drug Saf. 1997;16(6):355–65.
CAS
Article
Google Scholar
Pappa D, Stergioulas LK. Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions. Int J Data Sci Anal. 2019;8:1–23.
Article
Google Scholar
Sarker A, Nikfarjam A, Gonzalez G. Social media mining shared task workshop. Biocomputing 2016. Kohala Coast, Hawaii, USA: World Scientific; 2016. p. 581–92. Available from: http://www.worldscientific.com/doi/abs/10.1142/9789814749411_0054.
Jimeno-Yepes A, MacKinlay A, Han B, Chen Q. Identifying diseases, drugs, and symptoms in twitter. Stud Health Technol Inform. 2014;216:643–7.
Google Scholar
Weissenbacher D, Sarker A, Magge A, Daughton A, O’Connor K, Paul M, Gonzalez G. Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019. In: Proceedings of the Fourth Social Media Mining for Health Applications (# SMM4H) Workshop & Shared Task; 2019. p. 21–30.
Karimi S, Metke-Jimenez A, Kemp M, Wang C. Cadec: a corpus of adverse drug event annotations. J Biomed Inform. 2015;1(55):73–81.
Article
Google Scholar
Alvaro N, Miyao Y, Collier N. TwiMed: Twitter and PubMed comparable corpus of drugs, diseases, symptoms, and their relations. JMIR Public Health Surveill. 2017;3(2):e24.
Article
Google Scholar
Stanovsky G, Gruhl D, Mendes P. Recognizing mentions of adverse drug reaction in social media using knowledge-infused recurrent models. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017. p. 142–51.
Alimova I, Tutubalina E. Automated detection of adverse drug reactions from social media posts with machine learning. In: van der Aalst WMP, Ignatov DI, Khachay M, Kuznetsov SO, Lempitsky V, Lomazova IA, et al., editors. Analysis of images, social networks and texts. Cham: Springer International Publishing; 2018. p. 3–15. https://doi.org/10.1007/978-3-319-73013-4_1
Chapter
Google Scholar
Tang B, Hu J, Wang X, Chen Q. Recognizing continuous and discontinuous adverse drug reaction mentions from social media using LSTM-CRF. Wirel Commun Mob Comput. 2018;2018:1–8.
Google Scholar
Zhang T, Lin H, Ren Y, Yang L, Xu B, Yang Z, Wang J, Zhang Y. Adverse drug reaction detection via a multihop self-attention mechanism. BMC Bioinform. 2019;20(1):479.
Article
Google Scholar
WEB-RADR, https://web-radr.eu. Accessed 5 Apr 2019.
Dietrich J, Gattepaille LM, Grum BA, Jiri L, Lerch M, Sartori D, Wisniewski A. Adverse events in social media—development of a gold standard reference set: results from the WEB-RADR Project. Drug Saf. 2020. https://doi.org/10.1007/s40264-020-00912-9.
Article
PubMed
PubMed Central
Google Scholar
Pierce CE, Bouri K, Pamer C, Proestel S, Rodriguez HW, Van Le H, Freifeld CC, Brownstein JS, Walderhaug M, Edwards IR, Dasgupta N. Evaluation of facebook and twitter monitoring to detect safety signals for medical products: an analysis of recent FDA safety alerts. Drug Saf. 2017;40(4):317–31.
Article
Google Scholar
Hedfors S, Bergvall T, Gilbert M, Pierce C, Dasgupta N, Ellenius J. Improving the yield of relevant data for pharmacovigilance analysis by reducing search term complexity—a study on reddit data. Abstract Pharmacoepidemiol Drug Saf. 2016;25(S3):412–3.
Google Scholar
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, editors. Advances in neural information processing systems 26. Curran Associates, Inc.; 2013. p. 3111–9. Available from: http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf.
Godin F, Vandersmissen B, De Neve W, Van de Walle R (2015) Named entity recognition for Twitter microposts using distributed word representations. Workshop on Noisy User-generated Text, ACL 2015.
Wu S, Miller T, Masanz J, Coarr M, Halgrim S, Carrell D, Clark C. Negation’s not solved: generalizability versus optimizability in clinical natural language processing. PLoS One. 2014;9(11):e112774.
Article
Google Scholar
Ginn R, Pimpalkhute P, Nikfarjam A, Patki A, O’Connor K, Sarker A, et al. Mining Twitter for adverse drug reaction mentions: a corpus and classification benchmark. Proceedings of the fourth workshop on building and evaluating resources for health and biomedical text processing. 2014. p. 1–8.