Brief analysis the application of several signal monitoring methods in adverse drug events

DAI Fei; SHU Li-xin; CHU Cang; CHENG Sheng-xin; CHU Wen-gong

doi:10.3969/j.issn.1006-0111.2012.05.018

Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review, editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Name

E-mail

Phone

Title

Content

Verification Code

Objective To analyze several adverse drug reaction monitoring methods in home and aboard. Methods Literature analysis and system analysis method were used to illustrate a series of calculation methods in signal extraction of adverse drug reaction. Results China had gradually begun to use different signal monitoring algorithms to detect adverse drug reaction (ADR) signals. Conclusion Medication safety issue still needed to be paid close attention to in current and future, and drug safety monitoring was mainly through the post-marketing adverse signal monitoring to achieve. Different data mining algorithms for relevant signal monitoring at home and in abroad were summarized, which would provide reference for our adverse drug reaction signal processing, and do good to the drug safety alert.

Brief analysis the application of several signal monitoring methods in adverse drug events

Department of Pharmacy Administration, Second Military Medical University, Shanghai 200433, China

Received Date: 2011-11-24

Rev Recd Date: 2012-05-16

Key words:

Abstract: Objective To analyze several adverse drug reaction monitoring methods in home and aboard. Methods Literature analysis and system analysis method were used to illustrate a series of calculation methods in signal extraction of adverse drug reaction. Results China had gradually begun to use different signal monitoring algorithms to detect adverse drug reaction (ADR) signals. Conclusion Medication safety issue still needed to be paid close attention to in current and future, and drug safety monitoring was mainly through the post-marketing adverse signal monitoring to achieve. Different data mining algorithms for relevant signal monitoring at home and in abroad were summarized, which would provide reference for our adverse drug reaction signal processing, and do good to the drug safety alert.

HTML

Reference (13)

[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.

Message Board

Brief analysis the application of several signal monitoring methods in adverse drug events

doi: 10.3969/j.issn.1006-0111.2012.05.018

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views