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Data-Driven Extraction of Quantitative Multi-dimensional Associations of Cardiovascular Drugs and Adverse Drug Reactions

  • Upasana Chutia
  • Jerry W. Sangma
  • Vipin PalEmail author
  • YogitaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1005)

Abstract

Early detection of adverse drug reactions as a part of post-marketing surveillance is very crucial for saving a number of persons from unwanted consequences of drugs. Along with the drug, patient’s traits such as age, gender, weight, location are key factors for occurrence of adverse effects. The relationship between drug, patient attributes and adverse drug effects can be precisely represented by quantitative multi-dimensional association rules. But discovery of such rules faces the challenge of data sparsity because of the large number of possible side effects of a drug and fewer number of corresponding data records. In this paper, to address the data sparsity issue, we propose to use variable support based LPMiner technique for detecting quantitative multi-dimensional association rules. For experimental analysis, data corresponding to three cardiovascular drugs namely Rivaroxaban, Ranolazine and Alteplase has been taken from U.S. FDA Adverse Event Reporting System database. The experimental results show that based on LPMiner technique a number of association rules have been detected which went undetected in case of constant support based apriori and FP-Growth technique.

Keywords

Multi-dimensional associations rules Pharmacovigilance Data mining Adverse Drug Reactions (ADR) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Institute of Technology MeghalayaShillongIndia

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