Signal Detection of Drug Complications Applying Association Rule Learning for Stevens-Johnson Syndrome

• Shirakuni Yuko

  Graduate School of Pharmaceutical Sciences, Osaka University

• Okamoto Kousuke

  Graduate School of Pharmaceutical Sciences, Osaka University

• Kawashita Norihito

  Graduate School of Pharmaceutical Sciences, Osaka University Research Institute for Microbial Diseases, Osaka University

• Yasunaga Teruo

  Research Institute for Microbial Diseases, Osaka University

• Takagi Tatsuya

  Graduate School of Pharmaceutical Sciences, Osaka University Research Institute for Microbial Diseases, Osaka University

Bibliographic Information

Other Title

• 相関ルールの適用による、スティーブンスジョンソン症候群に関わる薬についてのシグナル検出

Abstract

The adverse events induced by drugs have been complicated, when two or more drugs are administrated for a patient. We selected "Stevens-Johnson Syndrome (SJS) " as a research object, which is one of the severe skin manifestations. The data source is a database constructed by the Food and Drug Administration (FDA). FDA's post-marketing safety surveillance program is supported by the Adverse Event Reporting System (AERS). AERS is designed with a computerized information database. To analyze the relationships between the concurrent medication and SJS in this study, we applied association rule learning. Our purpose is to propose an efficient procedure that enables the detection of signals for drugs related to an adverse event, without assuming the involvement of a specific drug. We defined new value K for the evaluation of existing signal detection. Association rule was evaluated according to criterion K value. As a result, it was suggested to obtain a strong signal by combining two concomitant drugs. Association rule learning in this study was applicable for the analysis of the relationships between adverse events and pairs of drugs.

Journal

• Journal of Computer Aided Chemistry

  Journal of Computer Aided Chemistry 10 118-127, 2009

  Division of Chemical Information and Computer Sciences The Chemical Society of Japan

Keywords

• association rule learning
• Stevens-Johnson Syndrome
• signal detection
• AERS

Details

• CRID: 1390001205108979840
• NII Article ID: 130004927274
• DOI: 10.2751/jcac.10.118
• ISSN: 13458647
• Web Site: https://www.jstage.jst.go.jp/article/jcac/10/0/10_0_118/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed