| アブストラクト | OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event. |
|---|
| 投稿者 | Cai, Ruichu; Liu, Mei; Hu, Yong; Melton, Brittany L; Matheny, Michael E; Xu, Hua; Duan, Lian; Waitman, Lemuel R |
|---|
| 組織名 | Faculty of Computer Science, Guangdong University of Technology, Guangzhou,;People's Republic of China. Electronic address: cairuichu@gmail.com.;Department of Internal Medicine, Division of Medical Informatics, University of;Kansas Medical Center, Kansas City, 66160, USA. Electronic address:;meiliu@kumc.edu.;Big Data Decision Institute, Jinan University, Guangzhou, People's Republic of;China.;School of Pharmacy, University of Kansas, Lawrence, USA.;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.;School of Biomedical Informatics, University of Texas Health Science Center at;Houston, Houston, USA.;Departent of Information Systems and Business Analytics, Hofstra University,;Hempstead, USA.;Kansas Medical Center, Kansas City, 66160, USA. |
|---|
