BackgroundTraditional Chinese Medicine (TCM) is a style of traditional medicine informed by modern medicine but built on a foundation of more than 2500 years of Chinese medical practice. According to statistics, TCM accounts for approximately 14% of total adverse drug reaction (ADR) spontaneous reporting data in China. Because of the complexity of the components in TCM formula, which makes it essentially different from Western medicine, it is critical to determine whether ADR reports of TCM should be analyzed independently.MethodsReports in the Chinese spontaneous reporting database between 2010 and 2011 were selected. The dataset was processed and divided into the total sample (all data) and the subsample (including TCM data only). Four different ADR signal detection methods-PRR, ROR, MHRA and IC- currently widely used in China, were applied for signal detection on the two samples. By comparison of experimental results, three of them—PRR, MHRA and IC—were chosen to do the experiment. We designed several indicators for performance evaluation such as R (recall ratio), P (precision ratio), and D (discrepancy ratio) based on the reference database and then constructed a decision tree for data classification based on such indicators.ResultsFor PRR: R1-R2 = 0.72%, P1-P2 = 0.16% and D = 0.92%; For MHRA: R1-R2 = 0.97%, P1-P2 = 0.20% and D = 1.18%; For IC: R1-R2 = 1.44%, P2-P1 = 4.06% and D = 4.72%. The threshold of R,Pand Dis set as 2%, 2% and 3% respectively. Based on the decision tree, the results are “separation” for PRR, MHRA and IC.ConclusionsIn order to improve the efficiency and accuracy of signal detection, we suggest that TCM data should be separated from the total sample when conducting analyses.
Data masking is an inborn defect of measures of disproportionality in adverse drug reactions signal detection. Some improved methods which used gender and age for data stratification only considered the patient-related confounding factors, ignoring the drug-related influencing factors. Due to a large number of reports and the high proportion of antibiotics in the Chinese spontaneous reporting database, this paper proposes a decision tree-stratification method for the minimization of the masking effect by integrating the relevant factors of patients and drugs. The adverse drug reaction monitoring reports of Jiangsu Province in China from 2011 to 2018 were selected for this study. First, the age division interval was determined based on the statistical analysis of antibiotic-related data. Secondly, correlation analysis was conducted based on the patient’s gender and age respectively with the drug category attributes. Thirdly, the decision tree based on age and gender was constructed by the J48 algorithm, which was used to determine if drugs belonged to antibiotics as a classification label. Fourthly, some performance evaluation indicators were constructed based on the data of drug package inserts as a standard signal library: recall, precision, and F (the arithmetic harmonic mean of recall and precision). Finally, four experiments were carried out by means of the proportional reporting ratio method: non-stratification (total data), gender-stratification, age-stratification and decision tree-stratification, and the performance of the signal detection results was compared. The experimental results showed that the decision tree-stratification was superior to the other three methods. Therefore, the data-masking effect can be further minimized by comprehensively considering the patient and drug-related confounding factors.
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.
Adverse drug reactions (ADRs) are a serious threat to people's lives and property safety. Currently, drug instructions are the main way for people to obtain information on ADRs. Due to drugs' limited pre-market clinical trials, adverse reactionsstated in drug instructions are often not sufficient. Avisualization platform for ADRs isput forward to address this problem. Adverse drug events (ADEs) data include actual clinical adverse reactions of drugs detected by drug monitoring administrationand can compensate for the insufficiency of drug instructions. Based on drug instructions and ADEs data, ADRVis isrealized by data analysis, model design and JAVA programming. ADRVis presentsthe relationship of 656 common drugs and their respective ADRs. Three case studies show that the platform has the capacities of visual presentation of ADRs and early warning of drug risks.The platform can provide people more rich information about drugs and help them understand ADRs more accurately and comprehensively.
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