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Background

The detection of signals of adverse drug events ADEs has increased because of the use of data mining algorithms in spontaneous reporting systems SRSs. However, different data mining algorithms have different traits and conditions for application. The objective of our study was to explore the application of association rule AR mining in ADE signal detection and to compare its performance with that of other algorithms.

Methodology-Principal Findings

Monte Carlo simulation was applied to generate drug-ADE reports randomly according to the characteristics of SRS datasets. Thousand simulated datasets were mined by AR and other algorithms. On average, 108,337 reports were generated by the Monte Carlo simulation. Based on the predefined criterion that 10% of the drug-ADE combinations were true signals, with RR equaling to 10, 4.9, 1.5, and 1.2, AR detected, on average, 284 suspected associations with a minimum support of 3 and a minimum lift of 1.2. The area under the receiver operating characteristic ROC curve of the AR was 0.788, which was equivalent to that shown for other algorithms. Additionally, AR was applied to reports submitted to the Shanghai SRS in 2009. Five hundred seventy combinations were detected using AR from 24,297 SRS reports, and they were compared with recognized ADEs identified by clinical experts and various other sources.

Conclusions-Significance

AR appears to be an effective method for ADE signal detection, both in simulated and real SRS datasets. The limitations of this method exposed in our study, i.e., a non-uniform thresholds setting and redundant rules, require further research.



Autor: Chao Wang , Xiao-Jing Guo , Jin-Fang Xu, Cheng Wu, Ya-Lin Sun, Xiao-Fei Ye, Wei Qian, Xiu-Qiang Ma, Wen-Min Du, Jia He

Fuente: http://plos.srce.hr/



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