基于BP神经网络的体外诊断试剂不良事件风险评估模型研究
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摘要
目的改进体外诊断试剂不良事件监测流程和手段,提高不良事件上报数量和质量,减少监管部门工作量,保障体外诊断试剂的安全有效。方法基于BP神经网络的前置过滤风险评估系统对体外诊断试剂不良事件进行初步评价,监管部门根据评价结果采取相应的应对措施。结果 BP神经网络通过历史数据学习对不良事件所作的风险评价结果与专家组的基本一致。结论利用BP神经网络可对不良事件进行风险评估,实现不良事件的风险信号聚集。
Objective To modify the monitoring process and means of adverse events in vitro diagnostic reagents, improve the quantity and quality of adverse events reported in vitro, and reduce the workload of regulatory authorities, eventually ensure the safety and effectiveness of in vitro diagnostic reagents. Methods The pre-filtering risk assessment system based on BP neural network was used to evaluate the adverse events of in vitro diagnostic reagents. According to the evaluation results, the administrative supervision departments took corresponding countermeasures. Results The BP neural network learned the historical data, and the risk evaluation results of the adverse events were basically consistent with the expert group. Conclusion BP neural network can be used to evaluate the risk of adverse events and achieve risk signal aggregation of adverse events.
Objective To modify the monitoring process and means of adverse events in vitro diagnostic reagents, improve the quantity and quality of adverse events reported in vitro, and reduce the workload of regulatory authorities, eventually ensure the safety and effectiveness of in vitro diagnostic reagents. Methods The pre-filtering risk assessment system based on BP neural network was used to evaluate the adverse events of in vitro diagnostic reagents. According to the evaluation results, the administrative supervision departments took corresponding countermeasures. Results The BP neural network learned the historical data, and the risk evaluation results of the adverse events were basically consistent with the expert group. Conclusion BP neural network can be used to evaluate the risk of adverse events and achieve risk signal aggregation of adverse events.
引文
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[16]王嵘冰,徐红艳,李波,等.BP神经网络隐含层节点数确定方法研究[J].计算机技术与发展,2018,28(4):31-35.
[2]杨笑鹤,马琳榕,王雯,等.体外诊断试剂产品全生命周期监管常见问题分析与建议[J].中国医疗器械杂志,2018,42(2):129-132.
[3]汪泽,钱虹,徐凤玲,等.体外诊断试剂的监督管理研究[J].中国医疗器械杂志,2005,29(2):124-130.
[4]李尧,王新敏,卞蓉蓉,等.体外诊断试剂519例可疑不良事件报告分析[J].药学与临床研究,2015(6):593-595.
[5]汪晶.我国药品不良反应自发呈报系统信号检测中压缩估计参数的探索[D].上海:第二军医大学,2015.
[6]李勇,白云,李川.基于小波分析与BP神经网络的PM_(10)浓度预测模型[J].环境监测管理与技术,2016,28(5):24-28.
[7]杨青,刘星星,陈瑞青,等.基于免疫系统的非常规突发事件风险识别模型[J].管理科学学报,2015,18(4):49-61.
[8]任文霞,茅鸯对,罗文华,等.医疗器械不良事件监测主体责任落实现状及对策研究[J].中国医疗器械杂志,2018,42(1):58-61.
[9]高洁.北京市建立医疗器械生产企业信用制度的成因与规划(连载)[J].首都食品与医药,2005,12(16):5-7.
[10]高洁.北京市建立医疗器械生产企业信用制度的成因与规划(连载)第二部分北京市医疗器械生产企业信用管理制度规划(二)[J].首都食品与医药,2005,12(18):6-8.
[11]任文霞,朱清,丁静,等.基于风险管理的医疗器械不良事件分级监测模式研究[J].中国医疗器械杂志,2018,42(3):215-218.
[12]王利平,刘任任.基于BP神经网络的红细胞识别[J].计算机与数字工程,2018,46(3):584-587.
[13]郝丽,胡大伟.基于BP神经网络和蜂群算法对在T-JIT环境下供应链协同风险的预警研究[J].公路交通科技,2018(6):112-120.
[14]胡华安,李梅.基于BP神经网络的装备采办风险控制研究[J].北京理工大学学报,2006(z1):104-109.
[15]叶茂春,李红艳,任慧霞.基于BP神经网络的上海城镇养老保险基金支付风险预警[J].中国老年学杂志,2018(15):3827-3832.
[16]王嵘冰,徐红艳,李波,等.BP神经网络隐含层节点数确定方法研究[J].计算机技术与发展,2018,28(4):31-35.