Corresponding author: Santiago Vilar, Department of Biomedical Informatics, Columbia University, USA; Department of Organic Chemistry, University of Santiago de Compostela, Spain. Tel.: +34-981-563100, Ext: 14938; Fax: +34-981-594912; E-mail: email@example.com
Santiago Vilar, Carol Friedman, George Hripcsak; Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform 2017 bbx010. 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.