Decision Tree-Based Data Stratification Method for the Minimization of the Masking Effect in Adverse Drug Reaction Signal Detection

Abstract

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.