Journal of Frontiers of Computer Science and Technology ›› 2009, Vol. 3 ›› Issue (2): 188-197.DOI: 10.3778/j.issn.1673-9418.2009.02.007

• 学术研究 • Previous Articles     Next Articles

Mining Naïve Intervention Rules in Birth Defect Data

ZHANG Yue1+, TANG Changjie1, LI Chuan1, ZHU Jun2, ZENG Chunqiu1, TANG Liang1, LIU Xianbin1   

  1. 1. College of Computer Science, Sichuan University, Chengdu 610064, China
    2. Birth Defects Supervising Centre of Western China Medical School, Sichuan University, Chengdu 610065, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-03-15 Published:2009-03-15
  • Contact: ZHANG Yue


张 悦1+,唐常杰1,李 川1,朱 军2,曾春秋1,唐 良1,刘显宾1   

  1. 1. 四川大学 计算机学院,成都 610064
    2. 中国出生缺陷监测中心 四川大学华西医学院,成都 610065
  • 通讯作者: 张 悦

Abstract: Mining naïve intervention rules from birth defects data is current hot topic concerned by both the birth defect research area and data mining fields. Taking birth defects data as background, this study aims the modeling of naïve intervention rules and discovering the possible causes of specific birth defects from the rough data. The main contributions include: Proposes a new concept called naïve intervention rule (NIR); Proposes and implements the algorithm to mine NIR; Conducts extensive experiments. The empirical result shows that the newly proposed algorithm successfully discovers the causes of prenatal birth defect, and provides evidences to suggest the redirection for intervention decision to reasonable degree.

Key words: naï, ve intervention rule, birth defect, delta

摘要: 出生缺陷干预规则挖掘是目前医学界和数据挖掘界共同关注的课题。以出生缺陷数据为背景,研究了朴素干预规则建模,并试图发现某些出生缺陷的可能致因。提出了朴素干预规则模型以及朴素干预规则挖掘算法。实验表明,提出的算法能有效挖掘出围产儿缺陷的致因,并为出生缺陷干预工程的政策制定提供致病因素的最佳状态调整方向。

关键词: 朴素干预规则, 出生缺陷, 变化量