[1] Moore N, Thiessard F, Begaud B. The history of disproportionality measures (reporting odds ratio, proportional reporting rates) in spontaneous reporting of adverse drug reactions[J]. Pharmacoepidemiol Drug Saf, 2005, 14(4):285.
[2] Rahul S, Amitava M. An urn model with applications to database performance evaluation[J].Computers &Operations Research, 1997, 24(4):289.
[3] Poluzzi E, Raschi E, Motola D,et al. De Ponti F. Antimicrobials and the risk of torsades de pointes:the contribution from data mining of the US FDA Adverse Event Reporting System[J]. Drug Saf, 2010, 33(4):303.
[4] Woo EJ,Ball R, Burwen DR,et al. Effects of stratification on data mining in the us vaccine adverse event reporting system[J].DrugSaf, 2008, 31(8):667.
[5] 陈炯华,魏永越,谢雁鸣. 基于SRS的中药上市后安全性信号监测方法介绍[J].中成药, 2010, (06):1036.
[6] 叶小飞,王海南,陈文,等. 数据挖掘在药物警戒中的应用[J].中国药物警戒, 2008, (01):36.
[7] Manfred Hauben, Sebastian Horn, Lester Reich Potential. Use of Data-Mining Algorithms for the Detection of surprise Adverse Drug Reactions[J]. Drug Saf, 2007, 30 (2):143.
[8] Bate A, Lindquist M, Edwards IR,et al. A Bayesian neural network method for adverse drug reaction singal generation[J].Eur J Clin Pharmacol,1998,54(4):315.
[9] Man YP, Dukyong Y. A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database[J]. Pharmacoepidemiol Drug Saf, 2011, 20(6):598.
[10] Kim J, Kim M, Ha JH,et al. Signal detection of methylphenidate by comparing a spontaneous reportingdatabase with a claims database[J]. Regul Toxicol Pharmacol, 2011, 61(2):154.
[11] Hochberg AM, Hauben M, Pearson RK,et al. An evaluation of three signal-detection algorithms using a highly inclusive reference event database[J]. Drug Saf, 2009, 32 (6):509.
[12] Bate A. Bayesian confidence propagation neural network[J]. Drug Saf, 2007, 30 (7):623.
[13] Kubota K, Koide D, Hirai T. Comparison of data mining methodologies using Japanese spontaneous reports[J]. Pharmacoepidemiol Drug Saf, 2004, 13(6):387.