Ji Yanqing, Ying Hao, Farber Margo S, Yen John, Dews Peter, Miller Richard E, Massanari R Michael
Department of Electrical and Computer Engineering Gonzaga University, Spokane, WA 99258, USA.
IEEE Trans Inf Technol Biomed. 2010 May;14(3):826-37. doi: 10.1109/TITB.2009.2037007. Epub 2009 Dec 11.
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 (<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 decision-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.
尽早在上市后监测中发现未知的药物不良反应（ADR）至关重要。当前上市后监测的方法主要依赖自发报告。这是一个被动监测系统，存在报告严重不足（报告率<10%）、延迟和报告不一致等局限性。我们提出了一种基于团队的新型智能代理软件系统方法，用于利用电子病历主动监测和检测潜在的感兴趣的ADR。我们设计了这样一个系统并将其命名为ADRMonitor。这些智能代理在位于不同地点的计算机上运行，能够持续自主地相互协作并协助人类用户（例如，食品药品监督管理局（FDA）、药物安全专业人员和医生）。这些代理应改进现有系统并加速ADR的早期识别。为了评估ADRMonitor相对于当前自发报告方法的性能，我们在各种条件下对ADR信号对（即药物与明显不良反应之间的潜在联系）的识别进行了模拟实验。实验涉及基于1000多名使用西沙必利治疗的真实患者创建的超过275,000名模拟患者，西沙必利上市七年，直至2000年因严重ADR被FDA撤市。利用自发报告方法和ADRMonitor的医疗保健专业人员分别通过我们最近开发的一种称为模糊识别启动决策（RPD）模型的通用认知决策模型推导的决策模型进行模拟。定量模拟结果表明：1）ADRMonitor检测到的真实ADR信号对数量比自发报告策略高6.6倍；2）即使是决策技能中等的ADRMonitor代理的ADR检测率也比自发报告高五倍；3）随着患者病例数量的增加，ADRMonitor能够显著更早地检测到ADR。
