title: Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports.
abstract: Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporting systems have been a major resource for drug safety surveillance that routinely collects adverse event reports from patients and healthcare professionals. In this study, we present a novel approach to discover DDIs from the Food and Drug Administration's adverse event reporting system.
  
  
  
  Data-driven discovery of DDI is an extremely challenging task because higher-order associations require analysis of all combinations of drugs and adverse events and accurate estimate of the relationships between drug combinations and adverse event require cause-and-effect inference. To efficiently identify causal relationships, we introduce the causal concept into association rule mining by developing a method called Causal Association Rule Discovery (CARD). The properties of V-structures in Bayesian Networks are utilized in the search for causal associations. To demonstrate feasibility, CARD is compared to the traditional association rule mining (AR) method in DDI identification.
  
  
  
  Based on physician evaluation of 100 randomly selected higher-order associations generated by CARD and AR, CARD is demonstrated to be more accurate in identifying known drug interactions compared to AR, 20% vs. 10% respectively. Moreover, CARD yielded a lower number of drug combinations that are unknown to interact, i.e., 50% for CARD and 79% for AR.
  
  
  
  Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event.
authors.affiliation: Faculty of Computer Science, Guangdong University of Technology, Guangzhou, People's Republic of China. Electronic address: cairuichu@gmail.com.
authors.forename: Ruichu
authors.initials: R
authors.lastname: Cai
authors.affiliation: Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, 66160, USA. Electronic address: meiliu@kumc.edu.
authors.forename: Mei
authors.initials: M
authors.lastname: Liu
authors.affiliation: Big Data Decision Institute, Jinan University, Guangzhou, People's Republic of China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Hu
authors.affiliation: School of Pharmacy, University of Kansas, Lawrence, USA.
authors.forename: Brittany L
authors.initials: BL
authors.lastname: Melton
authors.affiliation: Geriatric Research Education & Clinical Care, Tennessee Valley Healthcare System, Veteran's Health Administration, Nashville, USA; Department of Biomedical Informatics, Department of Medicine, Division of General Internal Medicine, & Department of Biostatistics, Vanderbilt University, Nashville, USA.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Matheny
authors.affiliation: School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
authors.affiliation: Departent of Information Systems and Business Analytics, Hofstra University, Hempstead, USA.
authors.forename: Lian
authors.initials: L
authors.lastname: Duan
authors.affiliation: Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, 66160, USA.
authors.forename: Lemuel R
authors.initials: LR
authors.lastname: Waitman
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D001499:Bayes Theorem; D057225:Data Mining; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D000069550:Machine Learning
keywords: Adverse drug reaction; Association rule; Causality; Drug-drug interaction