【24h】

Detecting DDI Using Ontology: Drug Mechanism of Action

机译:使用本体论检测DDI:药物作用机理

获取原文

摘要

Drug-drug interactions are generally harmful. This is usually manifested when the patient suffers from more than one disease for which drugs are prescribed and/or more than one drug is needed to be prescribed. The problem is made worse by the wide range of available drugs and the complexity which characterizes the variety of possible interactions or adverse effects. Determining potential drug-drug interactions (DDIs) is essential in any drug prescription. However, this process is not easy taking into consideration that medical and clinical data is continually increasing. Formal representation of the underlying knowledge is needed to provide comprehensive study of potential DDI. The ultimate aim of this work is to develop and use ontology for identifying DDI. DDI effects were classified into three types: toxic reaction, reduction effect or synergism effect. In this paper, the mechanism of action of drugs was considered to describe the proven general properties and action of drugs. Three scenarios were created to show the ability of description Logic (DL) reasoner of the proposed ontology to provide a proper classification of the DDI effects. In another words, describing the characteristics of any class will enable the DL reasoner to automatically recognize any individual that is an instance of this class. The study shows an efficient classification based on the defined ontology. The study relies on five dug families: Angiotensin-converting enzyme (ACE) inhibitors, Angiotensin II receptor blockers (ARBs), Beta blockers, Broad-spectrum penicillin's and Nonsteroidal anti-inflammatory drugs (NSAIDs).
机译:药物相互作用通常是有害的。这通常在患者遭受多于一种处方药和/或需要多于一种药物的疾病时表现出来。由于可用药物种类繁多以及表征各种可能的相互作用或不良反应的复杂性,这个问题变得更加严重。在任何药物处方中,确定潜在的药物-药物相互作用(DDI)都是必不可少的。但是,考虑到医学和临床数据在不断增加,这个过程并不容易。需要对基础知识进行形式化表示才能对潜在DDI进行全面研究。这项工作的最终目的是开发和使用本体来识别DDI。 DDI的作用分为三种类型:毒性反应,还原作用或协同作用。在本文中,考虑了药物的作用机理来描述已证明的药物一般性质和作用。创建了三个方案,以显示所提出本体的描述逻辑(DL)推理机提供DDI效果的正确分类的能力。换句话说,描述任何类的特征将使DL推理程序能够自动识别作为该类实例的任何个人。研究显示了基于定义的本体的有效分类。该研究依赖于五个挖掘家族:血管紧张素转换酶(ACE)抑制剂,血管紧张素II受体阻滞剂(ARB),β阻滞剂,广谱青霉素和非甾体抗炎药(NSAID)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号