数据挖掘技术在药物警戒中的应用。
Application of data mining techniques in pharmacovigilance.
作者信息
Wilson Andrew M, Thabane Lehana, Holbrook Anne
机构信息
Division of Clinical Pharmacology, Department of Medicine, McMaster University, 105 Main Street East, Level P1, Hamilton, Ontario L8N 1G6, Canada.
出版信息
Br J Clin Pharmacol. 2004 Feb;57(2):127-34. doi: 10.1046/j.1365-2125.2003.01968.x.
AIMS
To discuss the potential use of data mining and knowledge discovery in databases for detection of adverse drug events (ADE) in pharmacovigilance.
METHODS
A literature search was conducted to identify articles, which contained details of data mining, signal generation or knowledge discovery in relation to adverse drug reactions or pharmacovigilance in medical databases.
RESULTS
ADEs are common and result in significant mortality, and despite existing systems drugs have been withdrawn due to ADEs many years after licensing. Knowledge discovery in databases (KDD) is a technique which may be used to detect potential ADEs more efficiently. KDD involves the selection of data variables and databases, data preprocessing, data mining and data interpretation and utilization. Data mining encompasses a number of statistical techniques including cluster analysis, link analysis, deviation detection and disproportionality assessment which can be utilized to determine the presence of and to assess the strength of ADE signals. Currently the only data mining methods to be used in pharmacovigilance are those of disproportionality, such as the Proportional Reporting Ratio and Information Component, which have been used to analyse the UK Yellow Card Scheme spontaneous reporting database and the WHO Uppsala Monitoring Centre database. The association of pericarditis with practolol but not with other beta-blockers, the association of captopril and other angiotensin-converting enzymes with cough, and the association of terfenadine with heart rate and rhythm disorders could be identified by mining the WHO database.
CONCLUSION
In view of the importance of ADEs and the development of massive data storage systems and powerful computer systems, the use of data mining techniques in knowledge discovery in medical databases is likely to be of increasing importance in the process of pharmacovigilance as they are likely to be able to detect signals earlier than using current methods.
目的
探讨数据挖掘和数据库知识发现技术在药物警戒中检测药物不良事件（ADE）的潜在应用。
方法
进行文献检索以识别包含医学数据库中与药物不良反应或药物警戒相关的数据挖掘、信号生成或知识发现细节的文章。
结果
ADE很常见且会导致显著的死亡率，尽管现有系统存在，但仍有药物在获批多年后因ADE而被撤市。数据库知识发现（KDD）是一种可用于更高效地检测潜在ADE的技术。KDD包括数据变量和数据库的选择、数据预处理、数据挖掘以及数据解释和利用。数据挖掘涵盖多种统计技术，包括聚类分析、关联分析、偏差检测和不成比例评估，可用于确定ADE信号的存在并评估其强度。目前在药物警戒中使用的唯一数据挖掘方法是不成比例法，如比例报告比值法和信息成分法，这些方法已用于分析英国黄卡计划自发报告数据库和世界卫生组织乌普萨拉监测中心数据库。通过挖掘世界卫生组织数据库，可以识别出心包炎与心得宁而非其他β受体阻滞剂的关联、卡托普利和其他血管紧张素转换酶与咳嗽的关联以及特非那定与心率和心律紊乱的关联。
结论
鉴于ADE的重要性以及海量数据存储系统和强大计算机系统的发展，在医学数据库知识发现中使用数据挖掘技术在药物警戒过程中可能会变得越来越重要，因为它们可能比使用当前方法更早地检测到信号。
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