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Early detection of causal relationships between drugs and their associated adverse drug reactions (ADRs) can prevent harmful consequences or even deaths. Rare ADRs cannot be detected by pre-marketing clinical trials due to limitations in their size and duration. Existing postmarketing surveillance methods mainly rely on spontaneous reporting which is limited by severe underreporting (<;10 percentage...
Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is passive and suffers from gross underreporting (<0% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent system approach for...
Adverse drug reaction (ADR) is big challenge in drug development process. The US Food and Drug Administration (FDA) collects a lot of ADR report cases. We develop a tool which integrates drug and drug target knowledge bases and builds up drug target networks whose topological analysis can reveal drug interaction complexity for every ADR report in FDA. We classify drugs using anatomical therapeutic...
Current postmarketing surveillance methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare adverse drug reactions (ADRs). We propose an active, multi-agent computer software system, where each agent is empowered with teamwork capabilities such as anticipating information needs, identifying...
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