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摘要翻译：
药物警戒数据库包括几个涉及药物和不良事件（AEs）的病例报告。一些方法被一致地应用来突出所有信号，即药物和AE之间的所有统计意义上的关联。这些方法适合于验证涉及一种或几种药物和AE的更复杂的关系（如证候或相互作用），但不涉及它们的识别。我们提出了一种基于形式概念分析(FCA)和不相称性度量的方法来提取这些关系。这种方法识别所有的药物和AEs集合，这些都是潜在的信号，综合征或相互作用。与以前没有FCA的不相称性分析相比，FCA的加入更有效地识别与伴随药物相关的假阳性。
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英文标题：
《Mining for adverse drug events with formal concept analysis》
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作者：
Alexander Estacio-Moreno, Yannick Toussaint, C\'edric Bousquet
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
The pharmacovigilance databases consist of several case reports involving drugs and adverse events (AEs). Some methods are applied consistently to highlight all signals, i.e. all statistically significant associations between a drug and an AE. These methods are appropriate for verification of more complex relationships involving one or several drug(s) and AE(s) (e.g; syndromes or interactions) but do not address the identification of them. We propose a method for the extraction of these relationships based on Formal Concept Analysis (FCA) associated with disproportionality measures. This method identifies all sets of drugs and AEs which are potential signals, syndromes or interactions. Compared to a previous experience of disproportionality analysis without FCA, the addition of FCA was more efficient for identifying false positives related to concomitant drugs.
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PDF链接：
https://arxiv.org/pdf/0901.4004
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