An Optimal Approach for Mining Rare Causal Associations to Detect ADR Signal Pairs

Abstract

Abstract- Adverse Drug Reaction (ADR) is one of the most important issues in the assessment of drug safety. In fact, many adverse drug reactions are not discovered during limited premarketing clinical trials; instead, they are only observed after long term post-marketing surveillance of drug usage. In light of this, the detection of adverse drug reactions, as early as possible, is an important topic of research for the pharmaceutical industry. Recently, large numbers of adverse events and the development of data mining technology have motivated the development of statistical and data mining methods for the detection of ADRs. These stand-alone methods, with no integration into knowledge discovery systems, are tedious and inconvenient for users and the processes for exploration are time-consuming. This paper proposes an interactive system platform for the detection of ADRs. By integrating an ADR data warehouse and innovative data mining techniques, the proposed system not only supports OLAP style multidimensional analysis of ADRs, but also allows the interactive discovery of associations between drugs and symptoms, called a drug-ADR association rule, which can be further, developed using other factors of interest to the user, such as demographic information. The experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined. Index Terms- In this paper, we try to employ a knowledgebased approach to capture the degree of causality of an event pair within each sequence and we are going to match the data which was previously referred or suggested for treatment. � It is majorly used for Immediate Treatment for patients. However, mining the relationships between Drug and its Signal Reaction will be treated by In-Experienced Physician’