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A Distributed, Collaborative Intelligent Agent System Approach for Proactive Postmarketing Drug Safety Surveillance

机译:主动售后药品安全监控的分布式,协作式智能代理系统方法

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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 a passive surveillance system and limited by gross underreporting (u0003C;10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent software system approach for proactively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. The intelligent agents, operating on computers located in different places, are capable of continuously and autonomously collaborating with each other and assisting the human users (e.g., the food and drug administration (FDA), drug safety professionals, and physicians). The agents should enhance current systems and accelerate early ADR identification. To evaluate the performance of the ADRMonitor with respect to the current spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275 000 simulated patients created on the basis of more than 1000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. Healthcare professionals utilizing the spontaneous reporting approach and the ADRMonitor were separately simulated by decision-making models derived from a general cognitive decision model called fuzzy recognition-primed decision (RPD) model that we recently developed. The quantitative simulation results show that 1) the number of true ADR signal pairs detected by the ADRMonitor is 6.6 times higher than that by the spontaneous reporting strategy; 2) the ADR detection rate of the ADRMonitor agents with even moderate -n-ndecision-making skills is five times higher than that of spontaneous reporting; and 3) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor.
机译:尽早在售后监测中发现未知的药物不良反应(ADR)非常重要。当前的上市后监视方法主要依靠自发报告。它是一个被动监视系统,受报告不足(u0003C;报告率10%),延迟和报告不一致的限制。我们提出了一种新颖的基于团队的智能代理软件系统方法,用于使用电子病历主动监视和检测潜在的潜在ADR。我们设计了这样的系统,并将其命名为ADRMonitor。在不同位置的计算机上运行的智能代理能够连续且自主地相互协作,并为人类用户(例如食品与药品管理局(FDA),药品安全专业人员和医师)提供协助。代理商应增强现有系统并加快早期ADR识别。为了评估ADRMonitor相对于当前自发报告方法的性能,我们在各种条件下进行了模拟实验,以识别ADR信号对(即药物与明显不良反应之间的潜在联系)。该实验涉及超过275 000名模拟患者,这些患者是在超过1000名接受西沙必利治疗的实际患者的基础上创建的,该药物在市场上已有7年的销售历史,直到2000年由于严重的ADR被FDA撤出。决策模型分别模拟了采用自发报告方法和ADRMonitor的医疗保健专业人员,这些决策模型源自我们最近开发的通用认知决策模型,即模糊识别主导决策(RPD)模型。定量仿真结果表明:1)ADRMonitor检测到的真实ADR信号对数量是自发报告策略的6.6倍; 2)具有中等决策能力的ADRMonitor代理的ADR检测率比自发报告高五倍; 3)随着患者病例数的增加,ADRMonitor可以更早地检测到ADR。

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