The study of drug/medical device recall data (i) analyses on recall data in Japan

Tomoyuki Nagata, Ryo Okuya, Masaomi Kimura, Michiko Ohkura, Fumito Tsuchiya

Research output: Chapter in Book/Report/Conference proceedingChapter


In Japan, there have been reports of more than 600 recalls per year for drugs, medical devices, cosmetics and other quasi-drugs. The Japanese Ministry of Health, Labor and Welfare defined a classification system referring to that defined by the FDA, which has been used to classify all recall cases since 2000. Nevertheless, a problem in assigning the cases to these classifications has emerged. In this study, we analyzed the recall data on medicines and medical devices disclosed by Japanese authorities to determine the reason for such misclassifications and analyzed them based on a text mining technique, the dependency-link method. As a result, we found that the confusion of two kinds of extent, namely the extent of health hazards and the extent of the possibility of hazards emerging could be causes of incorrect classification. We also found that the possibilities were categorized into the possibility of using, possibility of finding a defect, and possibility of taking action against hazards. We also proposed a novel dimension of classification based on these possibilities.

Original languageEnglish
Title of host publicationAdvances in Human Aspects of Healthcare
PublisherCRC Press
Number of pages10
ISBN (Electronic)9781439870228
ISBN (Print)9781439870211
Publication statusPublished - 2012 Jan 1


  • Medical equipment
  • Medicine
  • Recall
  • Text mining

ASJC Scopus subject areas

  • Engineering(all)


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