Bate A, Lindquist M, Edwards I R, Olsson S, Orre R, Lansner A, De Freitas R M
Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Sweden.
Eur J Clin Pharmacol. 1998 Jun;54(4):315-21. doi: 10.1007/s002280050466.
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.
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.
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 the 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 online.
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.
乌普萨拉监测中心代表世界卫生组织（WHO）国际药品监测合作计划的47个国家所维护的药品不良反应（ADR）数据库包含近200万份报告。它是世界上最大的此类数据库，并且每季度会新增约35,000份报告。寻找新的药品 - ADR信号的任务一直由一个专家小组执行，但面对如此大量的资料，这项任务艰巨。我们开发了一种灵活的自动化程序，用于从背景数据中寻找具有已知概率差异的新信号。
使用各种计算方法的数据挖掘已应用于多个学科。已开发出一种贝叶斯置信传播神经网络（BCPNN），它可以管理大型数据集，在处理不完整数据时具有鲁棒性，并且可用于复杂变量。利用信息理论，这样的工具非常适合寻找与其他变量的药品 - ADR组合，这些组合与存储数据的总体情况或存储数据的一部分相比具有高度相关性。该方法具有透明度，便于检查，并且对于不同类型的搜索具有灵活性。
使用BCPNN给出了一些时间扫描示例，展示了该技术早期发现信号（卡托普利 - 咳嗽）以及避免数据库中出现常见药物和ADR时的假阳性（地高辛 - 痤疮；地高辛 - 皮疹）的能力。还测试了将BCPNN常规应用于季度更新的情况，结果表明1004种疑似药品 - ADR组合达到了与总体情况有差异的97.5%置信水平。其中，307种为潜在严重ADR，其中53种与新药有关。后者中有12种未记录在《医师案头参考》的光盘版或《马丁代尔药物大典》中，也未出现在《药物不良反应周刊》在线版中。
结果表明，BCPNN可用于从WHO国际药品监测计划的数据集中检测重要信号。BCPNN将成为对大量自发报告的ADR进行专家评估的极其有用的辅助工具。
