Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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Violation Pattern Analysis for Good Manufacturing Practice for Medicine using t-SNE Based on Association Rule and Text Mining

우수 의약품 제조 기준 위반 패턴 인식을 위한 연관규칙과 텍스트 마이닝 기반 t-SNE분석

  • Jun-O, Lee (Dept. of Industrial Engineering, Yonsei University) ;
  • So Young, Sohn (Dept. of Industrial Engineering, Yonsei University)
  • 이준오 (연세대학교 산업공학과) ;
  • 손소영 (연세대학교 산업공학과)
  • Received : 2022.09.10
  • Accepted : 2022.11.01
  • Published : 2022.12.31
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Abstract

Purpose: The purpose of this study is to effectively detect violations that occur simultaneously against Good Manufacturing Practice, which were concealed by drug manufacturers. Methods: In this study, we present an analysis framework for analyzing regulatory violation patterns using Association Rule Mining (ARM), Text Mining, and t-distributed Stochastic Neighbor Embedding (t-SNE) to increase the effectiveness of on-site inspection. Results: A number of simultaneous violation patterns was discovered by applying Association Rule Mining to FDA's inspection data collected from October 2008 to February 2022. Among them there were 'concurrent violation patterns' derived from similar regulatory ranges of two or more regulations. These patterns do not help to predict violations that simultaneously appear but belong to different regulations. Those unnecessary patterns were excluded by applying t-SNE based on text-mining. Conclusion: Our proposed approach enables the recognition of simultaneous violation patterns during the on-site inspection. It is expected to decrease the detection time by increasing the likelihood of finding intentionally concealed violations.

Keywords

Acknowledgement

교신저자의 이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No.2020R1A2C2005026)

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