Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports.

Journal: Artificial intelligence in medicine
Published Date: Feb 10, 2017

Abstract

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.

Authors

  • Ruichu Cai
    Faculty of Computer Science, Guangdong University of Technology, Guangzhou, People's Republic of China. Electronic address: cairuichu@gmail.com.
  • Mei Liu
    Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Missouri, USA.
  • Yong Hu
    Big Data Decision Institute, Jinan University, Guangzhou, China.
  • Brittany L Melton
    School of Pharmacy, University of Kansas, Lawrence, USA.
  • Michael E Matheny
    Vanderbilt University School of Medicine, Nashville, TN.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Lian Duan
    Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
  • Lemuel R Waitman
    Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Missouri, USA.

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