药物警戒中的信号检测:信息学驱动方法在不同数据源中发现药物-药物相互作用信号的综述

Heba Ibrahim , A. Abdo , Ahmed M. El Kerdawy , A. Sharaf Eldin
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引用次数: 19

摘要

本文的目的是回顾信息学驱动方法在药物警戒领域的应用,重点是使用各种数据源发现药物-药物相互作用(DDI)安全信号。信号可以是新的安全信息或已知药物不良反应的新方面,可能与一种或多种药物有因果关系,需要进一步调查以接受或反驳。信号可以从不同的数据源检测到,如自发报告系统、科学文献、生物医学数据库和电子健康记录。这篇综述得到了以下事实的证实:ddi导致了6-30%的药物不良事件发生率的公共卫生问题。在本文中,我们回顾了作者在使用不同数据源的DDI信号检测中应用的信息学驱动方法。本文的目的不是费力地调查所有PV文献。作为替代方案,我们讨论了用于发现DDI信号的信息学驱动方法和各种数据源,并通过PV文献中的研究实例进行了强化。采用信息学驱动的方法可以补充和优化安全信号检测的实践。然而,应该进行进一步的研究来评估这些方法的效率,并解决在实际临床环境和监管机构中外部验证、实施和采用的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources

Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources

The objective of this article is to review the application of informatics-driven approaches in the pharmacovigilance field with focus on drug-drug interaction (DDI) safety signal discovery using various data sources. Signal can be a new safety information or new aspect to already known adverse drug reaction which is possibly causally related to a medication/medications that warrants further investigation to accept or refute. Signals can be detected from different data sources such as spontaneous reporting system, scientific literature, biomedical databases and electronic health records. This review is substantiated based on the fact that DDIs are contributing to a public health problem represented in 6-30% adverse drug event occurrences. In this article, we review informatics-driven approaches applied by authors focusing on DDI signal detection using different data sources. The aim of this article is not to laboriously survey all PV literature. As an alternative, we discussed informatics-driven methods used to discover DDI signals and various data sources reinforced with instances of studies from PV literature. The adoption of informatics-driven approaches can complement and optimize the practice of safety signal detection. However, further researches should be carried out to evaluate the efficiency of those approaches and to address the limitations of external validation, implementation and adoption in real clinical environments and by the regulatory bodies.

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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
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