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文章基本信息

  • 标题:Anomaly Detection of Clinical Behavior Sequences
  • 本地全文:下载
  • 作者:Hebiao Yang ; Xiaodong Yuan
  • 期刊名称:Computer and Information Science
  • 印刷版ISSN:1913-8989
  • 电子版ISSN:1913-8997
  • 出版年度:2010
  • 卷号:3
  • 期号:3
  • 页码:197
  • DOI:10.5539/cis.v3n3p197
  • 出版社:Canadian Center of Science and Education
  • 摘要:

    The identification of abnormal clinical behavior during the process of treatments is of great significance for regulating the standard medical behavior. Due to clinical behavior constrained by time, and the timing of subsequence, GSP algorithm was modified in the present paper, and described the timing of subsequence by the introduction of the concept of legal subsequences in order to detect the frequent patterns in sequences; sequence association rules in accordance with the characteristics of territorial behavior were screened using association rule methods in order to establish rule base; Comparing the similarity between the detected frequent patterns and normal behavior rules, anomaly detection of the detected behavior was operated and the validity of the methods was verified through experiments.

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