Cited by

• Perspectives on the use of data mining in pharmaco-vigilance.

  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. Almenoff J, et al. Drug Saf. 2005;28(11):981-1007. doi: 10.2165/00002018-200528110-00002. Drug Saf. 2005. PMID: 16231953 Review.
• aer2vec: Distributed Representations of Adverse Event Reporting System Data as a Means to Identify Drug/Side-Effect Associations.

  Portanova J, Murray N, Mower J, Subramanian D, Cohen T. Portanova J, et al. AMIA Annu Symp Proc. 2020 Mar 4;2019:717-726. eCollection 2019. AMIA Annu Symp Proc. 2020. PMID: 32308867 Free PMC article.
• Assessment of Reye's syndrome profile with data from the US Food and Drug Administration Adverse Event Reporting System and the Japanese Adverse Drug Event Report databases using the disproportionality analysis.

  Matsumoto K, Hasegawa S, Nakao S, Shimada K, Mukai R, Tanaka M, Satake R, Yoshida Y, Goto F, Inoue M, Ikesue H, Iguchi K, Hashida T, Nakamura M. Matsumoto K, et al. SAGE Open Med. 2020 Nov 20;8:2050312120974176. doi: 10.1177/2050312120974176. eCollection 2020. SAGE Open Med. 2020. PMID: 33282307 Free PMC article.
• Cardiovascular adverse events in oncology trials: understanding and appreciating the differences between clinical trial data and real-world reports.

  Ewer MS, Herson J. Ewer MS, et al. Cardiooncology. 2022 Jul 19;8(1):13. doi: 10.1186/s40959-022-00139-w. Cardiooncology. 2022. PMID: 35854393 Free PMC article. Review.
• A Comparison Study of Algorithms to Detect Drug-Adverse Event Associations: Frequentist, Bayesian, and Machine-Learning Approaches.

  Pham M, Cheng F, Ramachandran K. Pham M, et al. Drug Saf. 2019 Jun;42(6):743-750. doi: 10.1007/s40264-018-00792-0. Drug Saf. 2019. PMID: 30762164