美国食品药品监督管理局自发报告系统中潜在药物相互作用不良反应的统计挖掘
Statistical Mining of Potential Drug Interaction Adverse Effects in FDA's Spontaneous Reporting System.
作者信息
Harpaz Rave, Haerian Krystl, Chase Herbert S, Friedman Carol
机构信息
Dept of Biomedical Informatics, Columbia University, New York, NY.
出版信息
AMIA Annu Symp Proc. 2010 Nov 13;2010:281-5.
Abstract
Many adverse drug effects (ADEs) can be attributed to drug interactions. Spontaneous reporting systems (SRS) provide a rich opportunity to detect novel post-marketed drug interaction adverse effects (DIAEs), as they include populations not well represented in clinical trials. However, their identification in SRS is nontrivial. Most existing research have addressed the statistical issues used to test or verify DIAEs, but not their identification as part of a systematic large scale database-wide mining process as discussed in this work. This paper examines the application of a highly optimized and tailored implementation of the Apriori algorithm, as well as methods addressing data quality issues, to the identification of DIAEs in FDAs SRS.
摘要
许多药物不良反应（ADEs）可归因于药物相互作用。自发报告系统（SRS）为检测新的上市后药物相互作用不良反应（DIAEs）提供了丰富的机会，因为它们纳入了临床试验中未充分体现的人群。然而，在SRS中识别这些不良反应并非易事。大多数现有研究都关注用于检验或验证DIAEs的统计问题，而非像本文所讨论的那样，将其作为系统的大规模全数据库挖掘过程的一部分来进行识别。本文探讨了对Apriori算法进行高度优化和定制的实现方法，以及解决数据质量问题的方法，用于在FDA的SRS中识别DIAEs。
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