Department of Biomedical Informatics, Columbia University, New York, USA.
Department of Organic Chemistry, University of Santiago de Compostela, Spain.
Brief Bioinform. 2018 Sep 28;19(5):863-877. doi: 10.1093/bib/bbx010.
Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
药物-药物相互作用（DDI）是药物开发和上市后药物警戒中的一个重要关注点。它们被认为是许多药物不良反应的原因，使患者面临更高的风险，并增加公共卫生系统的成本。监测和发现可能对人群造成伤害的 DDI 的方法是药物安全研究人员的主要目标。在这里，我们回顾了使用数据挖掘技术检测对患者有影响的 DDI 的不同方法和最新进展。我们专注于不同药物警戒源的数据挖掘，例如美国食品和药物管理局不良事件报告系统和医疗机构的电子健康记录，以及使用科学生物医学文献和社交媒体中可用的叙述性文本进行的各种数据挖掘研究。我们关注这些方法的优势，但也进一步解释了相关的挑战。数据挖掘在分析 DDI 方面有重要的应用，这些应用可以显示相互作用作为不良反应原因的影响，提取相互作用以创建知识数据集和黄金标准，并发现新的和危险的 DDI。
