title: iADRs: towards online adverse drug reaction analysis.
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 pre-marketing 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.
authors: Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan.
authors: Wen-Yang
authors: WY
authors: Lin
authors: He-Yi
authors: HY
authors: Li
authors: Jhih-Wei
authors: JW
authors: Du
authors: Wen-Yu
authors: WY
authors: Feng
authors: Chiao-Feng
authors: CF
authors: Lo
authors: Von-Wun
authors: VW
authors: Soo
keywords: Adverse drug reaction; Associative classification; Contingency cube; Data mining; Data warehouse; Pharmacovigilance