Identifying adverse drug reaction signal pairs by a multi-agent intelligent system with fuzzy decision model

Several thousands of drugs are currently available on the U.S. market. A complete understanding of the safe use of drugs is not possible at the time when drug is developed or marketed. At that time, the safety information is only obtained from a few thousand people in a typical pre-marketing clinical trial. Clinical trials are not capable of detecting rare adverse drug reactions (ADRs) because of limitations in sample size and trial duration. Early detection of unknown ADRs could save lives and prevent unnecessary hospitalizations. Current methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare ADRs. In this paper we propose a team-based multi-agent intelligent system approach for proactively detecting potential ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions). The basic idea is that intelligent agents are capable of collaborating with one another by sharing information and knowledge which will accelerate the process of detecting ADR signal pairs. Each agent is equipped with a fuzzy inference engine, which enables it to find the causal relationship between a drug and a potential ADR (i.e., a signal pair). To evaluate our approach, we designed a four-agent system and implemented it using JADE and FuzzyJess software packages. We choose four because it is representative enough while computing time is still reasonable. To assess the performance of the proposed system, we conducted a simulation experiment that involved over 10,000 patients treated by the drug Lisinopril at the Veterans Affairs Medical Center in Detroit between 2005 and 2008. The preliminary results indicate that the agents can successfully collaborate in finding signal pairs.