ShareHub
The ShareHub platform hosts resources from a variety of TraCS services.
To view available content, select a folder below. To download content, select "download" to the right of the file name. To complete the download, you must agree to the license associated with the content.
NOTE: To download resources, you must be signed in. To create a TraCS account, visit tracs.unc.edu/register.
Only registered and logged in users can download files from this category.
UNC Pharmacoepidemiology Lecture Series COVID-19 Journal Club- CVD, Drug Therapy and Mortality in COVID-19 by Mehra et al. 2020, led by Dr. Michele Jonsson-Funk and Sara Dejene
Compressed file includes 1) PDF of article 2) Power Point 3) recording of session
COVID-19 Journal Club- Immortal Time Bias and Real World Data (Anticoagulation in hospitalized COVID-19 Patients) This download includes 1) PDFs of each article 2) audio from presentation and 3) Power Point Slides
Machine Learning Seminar Series: Promise and Perils of Machine Learning in Clinical Research COVID-19 Journal Club-"BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting” by Dagan et al. 2020 in a Nationwide Mass Vaccination Setting” by Dagan et al. 2020, the download includes slides, the article and recording of the session.
Machine Learning Seminar Series: Machine Learning Use Cases 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
Machine Learning Seminar Series: CLARK! 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.
COVID-19 Journal Club- Recent Safety Signal and Updates on COVID-19 Vaccines: Astrazeneca and J&J Machine Learning Seminar Series:Recent Machine Learning Developments to Advance Precision Medicine Real World Evidence in the COVID Era: Which treatments work 
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