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我院药品不良反应/事件主动监测系统构建 被引量:16

Construction of active monitoring system for adverse drug reactions and adverse drug events in our hospital
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摘要 目的:构建药品不良反应/事件主动监测系统,为临床提供用药安全信息,及时发现并快速上报药品不良反应(adverse drug reaction,ADR)/事件(adverse drug event,ADE),实现基于该系统的ADR/ADE真实世界研究条件。方法:联合"触发器原理"与"贝叶斯置信传播神经网络法"挖掘医院信息系统的ADR/ADE信号,由药师负责建立规则、布局功能模块以及对效果进行评估验证,由软件工程师负责编写计算机程序实现。结果:建立了较全面的监测规则,实现了实时预警与回顾性研究数据快速提取,完成了ADR/ADE主动监测平台的搭建。结论:ADR/ADE实时预警有利于及时处置,减少药品危害,并提高上报效能,快速筛选数据的功能为上市后药品安全性再评价提供便利,对建立药品安全性监测与评价信息技术平台有重要意义。 OBJECTIVE To construct an active monitoring system for adverse drug reactions/events,provide medication safety information for clinical use,timely detect and rapidly report adverse drug reaction(ADR)/event(ADE),and realize the real world study conditions of ADR/ADE based on the system.METHODS"Trigger Principle"and"Bayesian Confidence Propagation Neural Network Method"were combined to detect ADR/ADE signals of hospital information system,and the pharmacist was responsible for establishing rules,layout of function modules and evaluating and verification effects,and the software engineer was responsible for compiling computer program to realize the system.RESULTS A comprehensive ADR/ADE monitoring platform is established,which realizes the real-time early warning and rapid retrospective study data extraction.CONCLUSION ADR/ADE real-time warning is beneficial to timely disposal,reduce drug hazards and improve reporting efficiency.The function of rapid screening data provides convenience for post-marketing drug safety re-evaluation,and is of great significance for establishing information technology platform for drug safety monitoring and evaluation.
作者 吴晓燕 张海霞 葛卫红 WU Xiao-yan;ZHANG Hai-xia;GE Wei-hong(Department of Pharmacy,Drum Tower Hospital Affiliated to Medical School of Nanjing University,Jiangsu Nanjing 210008,China)
出处 《中国医院药学杂志》 CAS 北大核心 2020年第4期437-442,共6页 Chinese Journal of Hospital Pharmacy
基金 江苏省药学会-奥赛康临床药学基金科研项目(编号:A201705)。
关键词 药品不良反应/事件 触发器 贝叶斯置信传播神经网络 主动监测 真实世界研究 adverse drug reactions/events triggers bayesian confidence propagation neural network active monitoring real world study
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