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  1. Data Mining

Data Mining at the Center for Drug Evaluation and Research

The FDA’s Adverse Event Reporting System (FAERS) database is the primary resource for identifying new adverse events of drugs marketed in the United States although supplementary methods are being evaluated. FAERS is a spontaneous (voluntary) adverse event reporting system and receives reports from a variety of sources, including (but not limited to) patients, physicians, pharmacists, nurses, other healthcare providers, researchers, attorneys, and pharmaceutical manufacturers. FAERS contains both domestic and international reports.

Initiatives of interest

200 Label Natural Language Processing Challenge:

Voice of the Patient

List of Data Mining Publications Related to Drug Safety

Sorbello A, Ripple A, Tonning J, Munoz M, Hasan R, Ly T, Francis H, Bodenreider O. Harnessing scientific literature reports for pharmacovigilance. Prototype software analytical tool development and usability testing. Appl Clin Inform. 2017 Mar 22;8(1):291-305.

Winnenburg R, Sorbello A, Ripple A, Harpaz R, Tonning J, Szarfman A, Francis H, Bodenreider O. Leveraging MEDLINE indexing for pharmacovigilance – Inherent limitations and mitigation strategies. J Biomed Inform. 2015 Oct;57:425-35.

Duggirala HJ, Tonning JM, Smith E, Bright RA, Baker JD, Ball R, Bell C, Bright-Ponte SJ, Botsis T, Bouri K, Boyer M, Burkhart K, Condrey GS, Chen JJ, Chirtel S, Filice RW, Francis H, Jiang H, Levine J, Martin D, Oladipo T, O'Neill R, Palmer LA, Paredes A, Rochester G, Sholtes D, Szarfman A, Wong HL, Xu Z, Kass-Hout T. Use of data mining at the Food and Drug Administration. J Am Med Inform Assoc. 2016 Mar;23(2):428-34.

Brinker AD, Lyndly J, Tonning J, Moeny D, Levine JG, Avigan MI. Profiling cumulative proportional reporting ratios of drug-induced liver injury in the FDA Adverse Event Reporting System (FAERS) database. Drug Saf. 2013 Dec;36(12):1169-78.

Kim PW, Sorbello AF, Wassel RT, Pham TM, Tonning JM, Nambiar S. Eosinophilic pneumonia in patients treated with daptomycin: review of the literature and US FDA adverse event reporting system reports. Drug Saf. 2012 Jun 1;35(6):447-57.

Rivkees SA, Szarfman A. Dissimilar hepatotoxicity profiles of propylthiouracil and methimazole in children. J Clin Endocrinol Metab. 95:3260-7. 2010.

Clinical Trials Handbook, by Shayne Cox Gad. pp 373-396, Published by John Wiley and Sons, Inc. 2009. Published Online: 15 March 2010. Pharmaceutical Sciences Encyclopedia: Drug Discovery, Development, and Manufacturing. Published by John Wiley and Sons, Inc. 2010.

Szarfman A, Tonning JM, Levine JG, Doraiswamy PM. Atypical antipsychotics and pituitary tumors: a pharmacovigilance study. Pharmacotherapy. 2006 Jun;26(6):748-58.

Szarfman A, Doraiswamy PM, Tonning JM, Levine JG. Association between pathologic gambling and parkinsonian therapy as detected in the Food and Drug Administration Adverse Event database. Arch Neurol. 2006 Feb;63(2):299-300.

Levine JG, Tonning JM, Szarfman A. Reply: The evaluation of data mining methods for the simultaneous and systematic detection of safety signals in large databases: lessons to be learned. Br J Clin Pharmacol. 2006 Jan;61(1):105-13.

Szarfman A, Levine JG, Tonning JM, A new paradigm for analyzing adverse drug events. (Textbook Chapter). Computer Applications in Pharmaceutical Research and Development Sean Ekins (Editor), Binghe Wang (Series Editor) ISBN: 0-471-73779-8. Chapter 27. 2006.

Almenoff J, Tonning JM, Gould AL, Szarfman A, Hauben M, Ouellet-Hellstrom R, Ball R, Hornbuckle K, Walsh L, Yee C, Sacks ST, Yuen N, Patadia V, Blum M, Johnston M, Gerrits C, Seifert H, Lacroix K. Perspectives on the use of data mining in pharmaco-vigilance. Drug Saf. 2005;28(11):981-1007.

Szarfman A, Tonning JM, Doraiswamy PM. Pharmacovigilance in the 21st century: new systematic tools for an old problem. Pharmacotherapy. 2004 Sep;24(9):1099-104.

Almenoff JS, DuMouchel W, Kindman LA, Yang X, Fram D. Disproportionality analysis using empirical Bayes data mining: a tool for the evaluation of drug interactions in the post-marketing setting. Pharmacoepidemiol Drug Saf. 2003 Sep;12(6):517-21.

Fram, D.M., Almenoff, J. & DuMouchel, W. Empirical Bayesian datamining for discovering patterns in post-marketing drug safety. Proc. Knowledge Discov. Data Proc. KDD 359–368 (2003).

Szarfman A, Machado SG, O'Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database. Drug Saf. 2002;25(6):381-92.

DuMouchel, W. & Pregibon, D. Empirical Bayes screening for multi-item associations. In 7th ACM SigKDD International Conference on Knowledge Discovery and Data Mining, 67– 76, (ACM Press, San Francisco, 2001).

Harpaz R, Odgers D, Gaskin G, DuMouchel W, Winnenburg R, Bodenreider O, Ripple A, Szarfman A, Sorbello A, Horvitz E, White R, Shah N. A time-indexed reference standard of adverse drug reactions. Nature Sci. Data 1:140043 doi: 10.1038/sdata.2014.43 (2014).

Winnenburg R, Sorbello A, Bodenreider O. Exploring adverse drug events at the class level. Journal of Biomedical Semantics 2015; 6:18-27.

Chen M-C, Iyasu S, Sorbello A, Scarazzini L. Spontaneous Reporting and Pharmacovigilance Practice: USA. In: Andrews E, Moore N, eds. Mann’s Pharmacovigilance. 3rd ed. Chichester, West Sussex, UK: Wiley Blackwell; 2014:229-240.

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