Bush V. As we may think. The Atlantic. July 1945.
Yu AC. Methods in biomedical ontology. J Biomed Inform. 2006;39(3):252–66. doi:10.1016/j.jbi.2005.11.006.
PubMed
Article
Google Scholar
Marshall MS, Boyce R, Deus HF, Zhao J, Willighagen EL, Samwald M, et al. Emerging practices for mapping and linking life sciences data using RDF—a case series. Web Semant Sci Serv Agents World Wide Web. 2012;14:2–13.
Article
Google Scholar
Jacunski A, Tatonetti NP. Connecting the dots: applications of network medicine in pharmacology and disease. Clin Pharmacol Therap. 2013;94(6):659–69. doi:10.1038/clpt.2013.168.
CAS
Article
Google Scholar
Yeleswarapu S, Rao A, Joseph T, Saipradeep VG, Srinivasan R. A pipeline to extract drug-adverse event pairs from multiple data sources. BMC Med Inform Decis Mak. 2014;14(1):13.
PubMed Central
PubMed
Article
Google Scholar
DuMouchel W, Ryan PB, Schuemie MJ, Madigan D. Evaluation of disproportionality safety signaling applied to healthcare databases. Drug Saf. 2013;36(Suppl 1):S123–32. doi:10.1007/s40264-013-0106-y.
PubMed
Article
Google Scholar
Madigan D, Schuemie MJ, Ryan PB. Empirical performance of the case-control method: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36(Suppl 1):S73–82. doi:10.1007/s40264-013-0105-z.
PubMed
Article
Google Scholar
Ryan PB, Madigan D, Stang PE, Overhage JM, Racoosin JA, Hartzema AG. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Stat Med. 2012;31(30):4401–15. doi:10.1002/sim.5620.
PubMed
Article
Google Scholar
Ryan PB, Schuemie MJ. Evaluating performance of risk identification methods through a large-scale simulation of observational data. Drug Saf. 2013;36(Suppl 1):S171–80. doi:10.1007/s40264-013-0110-2.
PubMed
Article
Google Scholar
Ryan PB, Schuemie MJ, Gruber S, Zorych I, Madigan D. Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36(Suppl 1):S59–72. doi:10.1007/s40264-013-0099-6.
PubMed
Article
Google Scholar
Ryan PB, Schuemie MJ, Madigan D. Empirical performance of a self-controlled cohort method: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36(Suppl 1):S95–106. doi:10.1007/s40264-013-0101-3.
PubMed
Article
Google Scholar
Ryan PB, Stang PE, Overhage JM, Suchard MA, Hartzema AG, DuMouchel W, et al. A comparison of the empirical performance of methods for a risk identification system. Drug Saf. 2013;36(Suppl 1):S143–58. doi:10.1007/s40264-013-0108-9.
PubMed
Article
Google Scholar
Schuemie MJ, Madigan D, Ryan PB. Empirical performance of LGPS and LEOPARD: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36(Suppl 1):S133–42. doi:10.1007/s40264-013-0107-x.
PubMed
Article
Google Scholar
Suchard MA, Zorych I, Simpson SE, Schuemie MJ, Ryan PB, Madigan D. Empirical performance of the self-controlled case series design: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36(Suppl 1):S83–93. doi:10.1007/s40264-013-0100-4.
PubMed
Article
Google Scholar
Schuemie MJ, Coloma PM, Straatman H, Herings RM, Trifiro G, Matthews JN, et al. Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods. Med Care. 2012;50(10):890–7.
PubMed
Article
Google Scholar
Schuemie MJ, Gini R, Coloma PM, Straatman H, Herings RM, Pedersen L, et al. Replication of the OMOP experiment in Europe: evaluating methods for risk identification in electronic health record databases. Drug Saf. 2013;36(Suppl 1):S159–69. doi:10.1007/s40264-013-0109-8.
PubMed
Article
Google Scholar
Alvarez Y, Hidalgo A, Maignen F, Slattery J. Validation of statistical signal detection procedures in eudravigilance post-authorization data: a retrospective evaluation of the potential for earlier signalling. Drug Saf. 2010;33(6):475–87. doi:10.2165/11534410-000000000-00000.
PubMed
Article
Google Scholar
Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-driven prediction of drug effects and interactions. Sci Transl Med. 2012;4(125):125ra31 doi:10.1126/scitranslmed.3003377.
PubMed Central
PubMed
Article
Google Scholar
Hochberg AM, Hauben M, Pearson RK, O’Hara DJ, Reisinger SJ, Goldsmith DI, et al. An evaluation of three signal-detection algorithms using a highly inclusive reference event database. Drug Saf. 2009;32(6):509–25. doi:10.2165/00002018-200932060-00007.
PubMed
Article
Google Scholar
Norén GN, Bergvall T, Ryan PB, Juhlin K, Schuemie MJ, Madigan D. Empirical performance of the calibrated self-controlled cohort analysis within temporal pattern discovery: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36(Suppl 1):S107–21. doi:10.1007/s40264-013-0095-x.
PubMed
Article
Google Scholar
Ryan PB, Schuemie MJ, Welebob E, Duke J, Valentine S, Hartzema AG. Defining a reference set to support methodological research in drug safety. Drug Saf. 2013;36(Suppl 1):S33–47. doi:10.1007/s40264-013-0097-8.
PubMed
Article
Google Scholar
Coloma PM, Avillach P, Salvo F, Schuemie MJ, Ferrajolo C, Pariente A, et al. A reference standard for evaluation of methods for drug safety signal detection using electronic healthcare record databases. Drug Saf. 2013;36(1):13–23. doi:10.1007/s40264-012-0002-x.
CAS
PubMed
Article
Google Scholar
Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc. 2011;18(4):441–8. doi:10.1136/amiajnl-2011-000116.
PubMed Central
PubMed
Google Scholar
Lee D, de Keizer N, Lau F, Cornet R. Literature review of SNOMED CT use. JAMIA. 2014;21(e1):e11–9. doi:10.1136/amiajnl-2013-001636.
PubMed
Google Scholar
Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc. 2012;19(1):54–60. doi:10.1136/amiajnl-2011-000376.
PubMed Central
PubMed
Google Scholar
Defalco FJ, Ryan PB, Soledad Cepeda M. Applying standardized drug terminologies to observational healthcare databases: a case study on opioid exposure. Health Serv Outcomes Res Methodol. 2013;13(1):58–67. doi:10.1007/s10742-012-0102-1.
PubMed Central
PubMed
Article
Google Scholar
Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014;42(1):D1091–7. doi:10.1093/nar/gkt1068.
CAS
PubMed Central
PubMed
Article
Google Scholar
Schriml LM, Arze C, Nadendla S, Chang YW, Mazaitis M, Felix V, et al. Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res. 2012;40(Database issue):D940–6. doi:10.1093/nar/gkr972.
CAS
PubMed Central
PubMed
Article
Google Scholar
Zarin DA, Tse T, Williams RJ, Califf RM, Ide NC. The ClinicalTrials.gov results database—update and key issues. N Engl J Med. 2011;364(9):852–60. doi:10.1056/NEJMsa1012065.
CAS
PubMed Central
PubMed
Article
Google Scholar
Duke J, Friedlin J, Ryan P. A quantitative analysis of adverse events and “overwarning” in drug labeling. Arch Intern Med. 2011;171(10):944–6. doi:10.1001/archinternmed.2011.182.
PubMed
Article
Google Scholar
Duke JD, Friedlin J. ADESSA: a real-time decision support service for delivery of semantically coded adverse drug event data. AMIA Annu Symp Proc. 2010;2010:177–81.
PubMed Central
PubMed
Google Scholar
Agbabiaka TB, Savovic J, Ernst E. Methods for causality assessment of adverse drug reactions: a systematic review. Drug Saf. 2008;31(1):21–37.
PubMed
Article
Google Scholar
Karch FE, Lasagna L. Toward the operational identification of adverse drug reactions. Clin Pharmacol Therap. 1977;21(3):247–54.
CAS
Google Scholar
Karch FE, Smith CL, Kerzner B, Mazzullo JM, Weintraub M, Lasagna L. Adverse drug reactions—a matter of opinion. Clin Pharmacol Therap. 1976;19(5 Pt 1):489–92.
CAS
Google Scholar
Koh Y, Li SC. A new algorithm to identify the causality of adverse drug reactions. Drug Saf. 2005;28(12):1159–61.
PubMed
Article
Google Scholar
Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, et al. A method for estimating the probability of adverse drug-reactions. Clin Pharmacol Therap. 1981;30(2):239–45.
CAS
Article
Google Scholar
Koh Y, Yap CW, Li SC. A quantitative approach of using genetic algorithm in designing a probability scoring system of an adverse drug reaction assessment system. Int J Med Inform. 2008;77(6):421–30. doi:10.1016/j.ijmedinf.2007.08.010.
PubMed
Article
Google Scholar
Lanctot KL, Naranjo CA. Comparison of the Bayesian approach and a simple algorithm for assessment of adverse drug events. Clin Pharmacol Therap. 1995;58(6):692–8. doi:10.1016/0009-9236(95)90026-8.
CAS
Article
Google Scholar
Duke JD, Han X, Wang ZP, Subhadarshini A, Karnik SD, Li XC et al. Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions. Plos Comput Biol. 2012;8(8):e1002614. doi:10.1371/journal.pcbi.1002614.
CAS
PubMed Central
PubMed
Article
Google Scholar
Cami A, Arnold A, Manzi S, Reis B. Predicting adverse drug events using pharmacological network models. Sci Transl Med. 2011;3(114):114ra127. doi:10.1126/scitranslmed.3002774.
PubMed
Article
Google Scholar
Cami A, Manzi S, Arnold A, Reis BY. Pharmacointeraction network models predict unknown drug–drug interactions. Plos One. 2013;8(4):e61468. doi:10.1371/journal.pone.0061468.
CAS
PubMed Central
PubMed
Article
Google Scholar
Cheng FX, Li WH, Wang XC, Zhou YD, Wu ZR, Shen J, et al. Adverse drug events: database construction and in silico prediction. J Chem Inf Model. 2013;53(4):744–52. doi:10.1021/Ci4000079.
CAS
PubMed
Article
Google Scholar
Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Therap. 2012;91(6):1010–21. doi:10.1038/clpt.2012.50.
CAS
Article
Google Scholar
Juhlin K, Ye X, Star K, Norén GN. Outlier removal to uncover patterns in adverse drug reaction surveillance—a simple unmasking strategy. Pharmacoepidemiol Drug Saf. 2013;22(10):1119–29. doi:10.002/pds.3474.
PubMed
Google Scholar
Karimi G, Star K, Norén GN, Hagg S. The impact of duration of treatment on reported time-to-onset in spontaneous reporting systems for pharmacovigilance. PLoS One. 2013;8(7):e68938. doi:10.1371/journal.pone.0068938.
CAS
PubMed Central
PubMed
Article
Google Scholar
Duke J, Friedlin J, Li X. Consistency in the safety labeling of bioequivalent medications. Pharmacoepidemiol Drug Saf. 2013;22(3):294–301. doi:10.1002/pds.3351.
CAS
PubMed
Article
Google Scholar