書誌事項
- タイトル別名
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- Signal Detection of Drug Complications Applying Association Rule Learning for Stevens-Johnson Syndrome
抄録
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.
収録刊行物
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- Journal of Computer Aided Chemistry
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Journal of Computer Aided Chemistry 10 118-127, 2009
公益社団法人 日本化学会・情報化学部会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390001205108979840
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- NII論文ID
- 130004927274
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- ISSN
- 13458647
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可