Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance

被引：27
作者：
Henegar, Corneliu
Bousquet, Cedric
Louet, Agnes Lillo-Le
Degoulet, Patrice
Jaulent, Marie-Christine
机构：
[1] Univ Paris 05, INSERM, U729, Fac Med, F-75006 Paris, France
[2] Hop Europeen Georges Pompidou, Ctr Reg Pharmacovigilance, Paris, France
来源：
COMPUTERS IN BIOLOGY AND MEDICINE | 2006年 / 36卷 / 7-8期
关键词：
adverse drug reaction reporting systems; terminology; automatic data processing; knowledge representation (computer); description logic; ontological modeling;
D O I：
10.1016/j.compbiomed.2005.04.009
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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.
引用
收藏
页码：748 / 767
页数：20
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