Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance
Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance
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DOI：
10.1016/j.compbiomed.2005.04.009
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发表时间：
2006-07-01
影响因子：
7.7
通讯作者：
Jaulent, Marie-Christine
中科院分区：
文献类型：
--
作者：
Henegar, Corneliu;Bousquet, Cedric;Jaulent, Marie-Christine
药物警戒中的自动信号生成实现了无监督的统计机器学习技术，以便在自发报告系统中发现未知的药物不良反应（ADR）。以前没有讨论过发展成果评估编码所用术语的影响。全球范围内用于药物警戒病例的国际医学用语词典（MedDRA）未提供术语的正式定义。我们已经建立了一个ADR的本体来描述MedDRA术语的语义。本体包容和近似匹配推理允许更好地分组医学相关的条件。信号生成性能显著提高，但与建模相关的时间消耗仍然非常重要。(c)2005爱思唯尔有限公司保留所有权利。
Automated signal generation in pharmacovigilance implements unsupervised statistical machine learning techniques in order to discover unknown adverse drug reactions (ADR) in spontaneous reporting systems. The impact of the terminology used for coding ADRs has not been addressed previously. The Medical Dictionary for Regulatory Activities (MedDRA) used worldwide in pharmacovigilance cases does not provide formal definitions of terms. We have built an ontology of ADRs to describe semantics of MedDRA terms. Ontological subsumption and approximate matching inferences allow a better grouping of medically related conditions. Signal generation performances are significantly improved but time consumption related to modelization remains very important. (c) 2005 Elsevier Ltd. All rights reserved.