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First Conference on Big Data for Pharmacovigilance

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  1. Trifirò G, Sultana J, Bate A. From big data to smart data for pharmacovigilance: the role of healthcare databases and other emerging sources. Drug Saf. 2018;41(2):143–9.

    Article  PubMed  Google Scholar 

  2. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2:3.

    Article  PubMed  PubMed Central  Google Scholar 

  3. US Department of Health and Human Services, US Food and Drug Administration, Center for Devices and Radiological Health, Center for Biologics Evaluation and Research. Use of real-world evidence to support regulatory decision-making for medical devices. Guidance for industry and Food and Drug Administration staff. 2017. Accessed 8 Jul 2018.

  4. Pacurariu AC, Straus SM, Trifirò G, Schuemie MJ, Gini R, Herings R, et al. Useful interplay between spontaneous ADR reports and electronic healthcare records in signal detection. Drug Saf. 2015;38(12):1201–10.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Patadia VK, Schuemie MJ, Coloma PM, Herings R, van der Lei J, Sturkenboom M, et al. Can electronic health records databases complement spontaneous reporting system databases? A historical-reconstruction of the association of rofecoxib and acute myocardial infarction. Front Pharmacol. 2018;9:594.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Finan C, Gaulton A, Kruger FA, Lumbers RT, Shah T, Engmann J, et al. The druggable genome and support for target identification and validation in drug development. Sci Transl Med. 2017.

    Article  PubMed  Google Scholar 

  7. Lorberbaum T, Sampson KJ, Woosley RL, Kass RS, Tatonetti NP. An integrative data science pipeline to identify novel drug interactions that prolong the QT interval. Drug Saf. 2016;39(5):433–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Xiao C, Li Y, Baytas IM, Zhou J, Wang F. An MCEM framework for drug safety signal detection and combination from heterogeneous real world evidence. Sci Rep. 2018;8:1806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Comfort S, Perera S, Hudson Z, Dorrell D, Meireis S, Nagarajan M, et al. Sorting through the safety data haystack: using machine learning to identify individual case safety reports in social-digital media. Drug Saf. 2018;41(6):579–90.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Busby J, Mills K, Zhang S-D, Liberante FG, Cardwell CR. Selective serotonin reuptake inhibitor use and breast cancer survival: a population-based cohort study. Breast Cancer Res. 2018;20:4.

    Article  PubMed  PubMed Central  Google Scholar 

  11. US Food and Drug Administration. FDA’s Sentinel initiative: background. 2017. Accessed 9 Jul 2018.

  12. Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc. 2018;25:969–75.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Becker BFH, Avillach P, Romio S, van Mulligen EM, Weibel D, Sturkenboom MCJM, et al. CodeMapper: semiautomatic coding of case definitions: a contribution from the ADVANCE project. Pharmacoepidemiol Drug Saf. 2017;26(8):998–1005.

    Article  PubMed  PubMed Central  Google Scholar 

  14. McDonald SA, Nijsten D, Bollaerts K, Bauwens J, Praet N, van der Sande M, et al. Methodology for computing the burden of disease of adverse events following immunization. Pharmacoepidemiol Drug Saf. 2018;27(7):724–30.

    Article  PubMed  PubMed Central  Google Scholar 

  15. European Medicines Agency. Identifying opportunities for “big data” in medicines development and regulatory science: report from a workshop held by EMA on 14-15 November 2016. 2017. Accessed 8 Jul 2018.

  16. Schneeweiss S, Eichler H-G, Garcia-Altes A, Chinn C, Eggimann A-V, Garner S, et al. Real world data in adaptive biomedical innovation: a framework for generating evidence fit for decision-making. Clin Pharmacol Ther. 2016;100(6):633–46.

    Article  CAS  PubMed  Google Scholar 

  17. Lane S, Lynn E, Shakir S. Investigation assessing the publicly available evidence supporting postmarketing withdrawals, revocations and suspensions of marketing authorisations in the EU since 2012. BMJ Open. 2018;8:e019759.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Gagne J, Han X, Hennessy S, Leonard C, Chrischilles E, Carnahan R, et al. Successful comparison of US Food and Drug Administration Sentinel analysis tools to traditional approaches in quantifying a known drug-adverse event association. Clin Pharmacol Ther. 2016;100(5):558–64.

    Article  CAS  PubMed  Google Scholar 

  19. Zhou X, Douglas IJ, Shen R, Bate A. Signal detection for recently approved products: adapting and evaluating self-controlled case series method using a US claims and UK electronic medical records database. Drug Saf. 2018;41(5):523–36.

    Article  PubMed  Google Scholar 

  20. Caster O, Sandberg L, Bergvall T, Watson S, Norén GN. vigiRank for statistical signal detection in pharmacovigilance: first results from prospective real-world use. Pharmacoepidemiol Drug Saf. 2017;26:1006–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang SV, Schneeweiss S, Berger ML, Brown J, de Vries F, Douglas I, et al. Reporting to improve reproducibility and facilitate validity assessment for healthcare database studies V1.0. Pharmacoepidemiol Drug Saf. 2017;26:1018–32.

    Article  PubMed  PubMed Central  Google Scholar 

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The authors thank the speakers and attendees for the successful first conference on big data for pharmacovigilance.

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Correspondence to Jae Min.

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No funding was received for the preparation of this meeting report.

Conflict of interest

Jae Min is a full-time PhD student at the University of Florida and has received an honorarium for writing the meeting report. Her travel was supported by the Center for European Studies and the Graduate Student Council at the University of Florida. Vicki Osborne, Elizabeth Lynn, and Saad Shakir are employees of the Drug Safety Research Unit, who sponsored this conference. Jae Min, Elizabeth Lynn, Vicki Osborne, and Saad Shakir have no conflicts of interest that are directly relevant to the contents of this meeting report.

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Min, J., Osborne, V., Lynn, E. et al. First Conference on Big Data for Pharmacovigilance. Drug Saf 41, 1281–1284 (2018).

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