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Discovering unknown adverse drug reactions (ADRs) as early as possible is highly desirable. Current methods largely rely on passive spontaneous reports, which suffer from serious underreporting, latency, and inconsistent reporting. They are not ideal for early identification of ADRs. In this paper, we propose a multi-agent system approach for ADR detection. A multi-agent system is formed by a community of agents that exchange information and proactively help one another to achieve the goals set by the system designer. We show how agents, equipped with decision rules developed by the physicians on the team, can collaborate to detect signal pairs of potential ADRs. Using the popular agent language JADE and clinical information on 1,000 patients treated at the Detroit Veterans Affairs Medical Center, we have constructed a small group of agents and generated preliminary simulated detection results.