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Development of Mining model through reproducibility assessment in Adverse drug event surveillance system

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2009, 14(3), pp.183-192
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Youngho Lee 1 Youngmi Yoon 1 이병문 1 HeeJoung Hwang 1 강운구 1

1가천의과학대학교

Accredited

ABSTRACT

ADESS(Adverse drug event surveillance system) is the system which distinguishes adverse drug events using adverse drug signals. This system shows superior effectiveness in adverse drug surveillance than current methods such as volunteer reporting or char review. In this study, we built clinical data mart(CDM) for the development of ADESS. This CDM could obtain data reliability by applying data quality management and the most suitable clustering number(n=4) was gained through the reproducibility assessment in unsupervised learning techniques of knowledge discovery. As the result of analysis, by applying the clustering number(N=4), K-means, Kohonen, and two-step clustering models were produced and we confirmed that the K-means algorithm makes the most closest clustering to the result of adverse drug events.

Citation status

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