# Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
###### Abstract  
Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection. association rule; data mining; adverse drug reaction; signal detection +
Footnote †: journal: MedicalThe traditional ADR signal detection method is mainly based on disproportionality analysis (DPA). The proportional reporting ratio (PRR) method is the most basic signal detection algorithm in the early stage [3; 4; 5]. On this basis, the British Medicines and Healthcare Products Regulatory Agency (MHRA) combined the value of PRR with the target drug, the target ADR report number, and the Pearson Chi-square value as a more stable signal detection method, called the MHRA method [6]. At present, this method has been widely used by the Pharmacovigilance Center in the Netherlands, the Drug ADR Monitoring System in the United Kingdom, the Uppsala Monitoring Center of the World Health Organization (WHO, UMC), and the Drug ADR Spontaneous Reporting System in the United States [7]. However, the results of the MHRA method are easily affected by spontaneous reports. Although the MHRA method is determined by three metrics and the results are relatively stable and accurate, as the number of spontaneous reports increases, the value calculated by the formula of the index will inevitably decrease as the base increases due to the limitations of the metrics themselves. When the specified thresholds remain unchanged, the sensitivity of the MHRA method will decrease. As an important data mining method, association rules analysis (ARA) has been introduced into signal detection to solve the problems of drug safety. Through research on relevant papers, we found that (1) most of the researchers applied the ARA method to specific drugs and performed personalized analysis of the ADRs of some drugs. That is to say, researchers rarely used SRS for many drugs and a larger range of studies to detect ADR signals. (2) Most researchers used ARA as a preliminary screening tool for the detection of ADR signals. By calculating the Support, Confidence, or other metrics of each ADR combination, they filtered out the combinations that were not in the ideal range and used other algorithms to further detect the ADR signals in the selected high-quality combinations. This approach failed to tap the maximum potential of ARA, increased the research cost of data mining, and complicated the experiment. (3) When using ARA to filter data, only Support, Confidence, or other metrics were used as the screening criteria, and the performance of the criteria were not evaluated. Therefore, it was difficult to confirm whether the experimental results were the optimum.