A Bayesian neural network method for adverse drug reaction signal generation
A Bayesian neural network method for adverse drug reaction signal generation
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DOI：
10.1007/s002280050466
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发表时间：
1998-06-01
影响因子：
2.9
通讯作者：
De Freitas, RM
中科院分区：
文献类型：
--
作者：
Bate, A;Lindquist, M;De Freitas, RM
目的：乌普萨拉监测中心代表世界卫生组织（世卫组织）国际药物监测合作方案的47个国家持有的药物不良反应数据库包含近200万份报告。这是世界上最大的这类数据库，每季度增加约35000份新报告。一个专家小组已经完成了寻找新的药物不良反应信号的任务：但由于材料数量如此之大，这项任务令人望而生畏。我们已经开发了一个灵活的，自动化的程序，以找到新的信号与已知的概率差异的背景数据。方法：数据挖掘，使用各种计算方法，已被应用于各种学科。贝叶斯置信传播神经网络（BCPNN）已被开发，它可以管理大的数据集，在处理不完整的数据是强大的，并可以与复杂的变量。使用信息论，这种工具是理想的，用于发现药物-ADR组合与其他变量，这是高度相关的存储数据的一般性相比，或存储数据的一部分。该方法是透明的，易于检查和灵活的不同种类的search.Results：使用BCPNN，一些时间扫描的例子，给出了该技术的力量，发现信号早期（卡托普利咳嗽），并避免假阳性的一种常见的药物和ADR发生在数据库中（地高辛痤疮，地高辛皮疹）。还测试了BCPNN在季度更新中的常规应用，表明1004种可疑药物-ADR组合达到了97.5%的置信水平。其中，307例为潜在严重ADR，其中53例与新药相关。后者中有12个没有记录在《医生案头参考》或《马丁代尔药典外》的CD版中，也没有出现在《反应周刊》的在线版中。结果表明，BCPNN可以用于WHO国际药物监测计划数据集的重要信号检测，将是一个非常有用的辅助专家评估的非常大的数量。自发报告的ADR。
Objective: The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million reports. It is the largest database of this sort in the world, and about 35 000 new reports are added quarterly. The task of trying to find new drug-ADR signals has been carried out by an expert panel: but with such a large volume of material the task is daunting. We have developed a flexible, automated procedure to find new signals with known probability difference from the background data. Method: Data mining, using various computational approaches, has been applied in a variety of disciplines. A Bayesian confidence propagation neural network (BCPNN) has been developed which can manage large data sets, is robust in handling incomplete data, and may be used with complex variables. Using information theory, such a tool is ideal for finding drug-ADR combinations with other variables, which are highly associated compared to the generality of the stored data, or a section of the stored data. The method is transparent for easy checking and flexible for different kinds of search.Results: Using the BCPNN, some time scan examples are given which show the power of the technique to find signals early (captopril-coughing) and to avoid false positives where a common drug and ADRs occur in the database (digoxin-acne; digoxin-rash). A routine application of the BCPNN to a quarterly update is also tested, showing that 1004 suspected drug-ADR combinations reached the 97.5% confidence level of difference from :he generality. Of these, 307 were potentially serious ADRs, and of these 53 related to new drugs. Twelve of the latter were not recorded in the CD editions of The physician's Desk Reference or Martindale's Extra Pharmacopoea and did not appear in Reactions Weekly on-line.Conclusion: The results indicate that the BCPNN can be used in the detection of significant signals from the data set of the WHO Programme on International Drug Monitoring.The BCPNN will be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs.