QC Chart Mining(Scientific Data Mining)

スポンサーリンク

概要

• 論文の詳細を見る

• This paper presents a novel method: "QC Chart Mining", which aims at extracting systematic error patterns from quality control charts at a medical laboratory. In this paper we describe the basic principle pf a time decomposition mechanism for QC Chart Mining in order to detect substantial systematic errors, which might deteriorate clinical test data in their analytical processes. QC Chart Mining is used to recognize quality problems such as long-term trends and/or daily cyclic variations in analytical processes of clinical tests, then to improve the quality level over clinical laboratory medicine. Intensive experiments from both actual quality-control data and artificial data have revealed the validity of the proposed method. Our results have shown that the proposed method is useful and effective for quality managements in a medical laboratory.

• 一般社団法人情報処理学会の論文
• 2004-12-04

著者

• Terano Takao

  Graduate School Of Systems Management Tsukuba University:department Of Computational Intelligence An

• Inada Masanori

  Department Of Clinical Laboratory Toranomon Hospital:graduate School Of Systems Management Tsukuba U

• Terano Takao

  Graduate School Of Systems Management Tsukuba University:department Of Computational Intelligence An

関連論文

• WWW用クｰロンの法則に関する興味がわくビデオ教材の作成
• QC Chart Mining(Scientific Data Mining)
• QC Chart Mining(Scientific Data Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)

スポンサーリンク