Bate A, Lindquist M, Orre R, Edwards I R, Meyboom R H B
The Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Sweden.
Eur J Clin Pharmacol. 2002 Oct;58(7):483-90. doi: 10.1007/s00228-002-0484-z. Epub 2002 Sep 3.
The aim of this paper is to demonstrate the usefulness of the Bayesian Confidence Propagation Neural Network (BCPNN) in the detection of drug-specific and drug-group effects in the database of adverse drug reactions of the World Health Organization Programme for International Drug Monitoring.
Examples of drug-adverse reaction combinations highlighted by the BCPNN as quantitative associations were selected. The anatomical therapeutic chemical (ATC) group to which the drug belonged was then identified, and the information component (IC) was calculated for this ATC group and the adverse drug reaction (ADR). The IC of the ATC group with the ADR was then compared with the IC of the drug-ADR by plotting the change in IC and its 95% confidence limit over time for both.
The chosen examples show that the BCPNN data-mining approach can identify drug-specific as well as group effects. In the known examples that served as test cases, beta-blocking agents other than practolol are not associated with sclerosing peritonitis, but all angiotensin-converting enzyme inhibitors are associated with coughing, as are antihistamines with heart-rhythm disorders and antipsychotics with myocarditis. The recently identified association between antipsychotics and myocarditis remains even after consideration of concomitant medication.
The BCPNN can be used to improve the ability of a signal detection system to highlight group and drug-specific effects.
本文旨在证明贝叶斯置信传播神经网络（BCPNN）在世界卫生组织国际药物监测计划药物不良反应数据库中检测药物特异性和药物组效应方面的实用性。
选择被BCPNN突出显示为定量关联的药物-不良反应组合实例。然后确定药物所属的解剖学治疗学化学（ATC）组，并计算该ATC组与药物不良反应（ADR）的信息成分（IC）。通过绘制两者随时间变化的IC及其95%置信限，将ATC组与ADR的IC与药物-ADR的IC进行比较。
所选实例表明，BCPNN数据挖掘方法可以识别药物特异性效应以及组效应。在用作测试案例的已知实例中，除了心得宁之外的β受体阻滞剂与硬化性腹膜炎无关，但所有血管紧张素转换酶抑制剂都与咳嗽有关，抗组胺药与心律紊乱有关，抗精神病药与心肌炎有关。即使考虑了合并用药，最近发现的抗精神病药与心肌炎之间的关联仍然存在。
BCPNN可用于提高信号检测系统突出组效应和药物特异性效应的能力。
