Developing an Integrated Time-Series Data Mining Environment for Medical Data Mining
Developing an Integrated Time-Series Data Mining Environment for Medical Data Mining
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DOI：
10.1109/icdmw.2007.47
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
H. Abe;H. Yokoi;M. Ohsaki;Takahira Yamaguchi
中科院分区：
文献类型：
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作者：
H. Abe;H. Yokoi;M. Ohsaki;Takahira Yamaguchi
本文提出了一个用于医学数据挖掘的集成时间序列数据挖掘环境。医学时间序列数据挖掘是从医学数据库中获取有用的临床知识的关键问题之一。然而，在这样的医疗时间序列数据挖掘过程中，用户往往面临着数据预处理方法的选择/构建、挖掘算法的选择以及后处理来提炼数据挖掘过程的困难，就像其他数据挖掘过程中所表现的那样。为了从时间序列数据挖掘过程中为医学专家获取更多有价值的规则，我们设计了一个将时间序列模式提取方法、规则归纳方法和规则评估方法与可视化人机界面相结合的环境。在实现该环境后，我们对慢性肝炎患者的血/尿生化检测数据库进行了时间序列规则挖掘的案例研究。结果表明，基于时间序列模式提取的方法能够有效地发现有价值的临床病程规律。此外，我们还比较了时间序列模式提取方法与客观规则评估结果的差异。
In this paper, we present an integrated time-series data mining environment for medical data mining. Medical time-series data mining is one of key issues to get useful clinical knowledge from medical databases. However, users often face difficulties during such medical time-series data mining process for data preprocessing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process as shown in other data mining processes. To get more valuable rules for medical experts from a time-series data mining process, we have designed an environment which integrates time- series pattern extraction methods, rule induction methods and rule evaluation methods with visual human-system interface. After implementing this environment, we have done a case study to mine time- series rules from blood/urine biochemical test database on chronic hepatitis patients. The result shows the availability to find out valuable clinical course rules based on time-series pattern extraction. Furthermore, we compared the difference of time-series pattern extraction methods with objective rule evaluation results.