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Foundational Methods Paper for the Field with US FDA
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 safety. 2014 May;37(5):343-50.
https://link.springer.com/article/10.1007/s40264-014-0155-x
Consensus Recommendations for the European Union
van Stekelenborg J, Ellenius J, Maskell S, Bergvall T, Caster O, Dasgupta N, Dietrich J, Gama S, Lewis D, Newbould V, Brosch S. Recommendations for the use of social media in pharmacovigilance: lessons from IMI WEB-RADR. Drug safety. 2019 Dec;42(12):1393-407.
https://link.springer.com/article/10.1007/s40264-019-00858-7
Real-world Timeliness and Utility Study with US FDA
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 safety. 2017 Apr 1;40(4):317-31.
https://link.springer.com/content/pdf/10.1007/s40264-016-0491-0.pdf
Patient-centric Reporting of Side Effects in Social Media
Patel R, Belousov M, Jani M, Dasgupta N, Winokur C, Nenadic G, Dixon WG. Frequent discussion of insomnia and weight gain with glucocorticoid therapy: an analysis of Twitter posts. NPJ digital medicine. 2018 Feb 12;1(1):1-7.
https://www.nature.com/articles/s41746-017-0007-z
Responsibility of Pharmaceutical Industry (GSK) for Monitoring Side Effects
Powell GE, Seifert HA, Reblin T, Burstein PJ, Blowers J, Menius JA, Painter JL, Thomas M, Pierce CE, Rodriguez HW, Brownstein JS, Freifeld CC, Bell HG, Dasgupta N. Social media listening for routine post-marketing safety surveillance. Drug safety. 2016 May 1;39(5):443-54.
https://link.springer.com/article/10.1007%2Fs40264-015-0385-6
Limitations Compared to Traditional Pharmacovigilance (WHO-UMC)
Gattepaille LM, Hedfors Vidlin S, Bergvall T, Pierce CE, Ellenius J. Prospective evaluation of adverse event recognition systems in Twitter: Results from the Web-RADR project. Drug safety. 2020 Aug;43:797-808.
https://link.springer.com/content/pdf/10.1007/s40264-020-00942-3.pdf
The third session in the Machine Learning Seminar Series presented by the Comparative Effectiveness Research Service, this session is led by Dr. Buck Bohac, data analyst at NC TraCS discusses CLARK! a open source machine-learning classifier. This download includes a recording and slides.
CLARK! can be downloaded here.