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Discovering sequential disease patterns in medical databases using freespan mining and prefikspan mining approach

机译:使用自由采矿和采矿前采矿方法发现医学数据库中的连续疾病模式

摘要

Dr. Soetomo General Hospital had computerized their system to stored inpatient#65533s history. With lots of data to be analysis, one of the needs is a decision support system in order to anticipate the spread of the disease. Therefore the hospital need a system to provide the sequential pattern of disease. One of the sequential pattern mining algorithm is pattern growth based approach. The result is sequential pattern of disease from particular area in a time period based on inpatient#65533s history. Input from user are time period, minimum support, province, and multi-dimensional. The system built with Java Net Beans 6.7 and Oracle 10g. This research showed that FreeSpan and PrefikSpan produce the same output. However, FreeSpan is more appropriate for dr. Soetomo General Hospital because the proccesing time is faster.
机译:Soetomo总医院医生已经将他们的系统进行了计算机处理,以存储#65533患者的病史。由于要分析大量数据,因此需要一个决策支持系统来预测疾病的传播。因此,医院需要一种系统来提供疾病的顺序模式。顺序模式挖掘算法之一是基于模式增长的方法。结果是根据住院患者#65533s的历史记录在特定时间段内特定区域的疾病顺序模式。用户的输入是时间段,最小支持,省和多维。该系统使用Java Net Beans 6.7和Oracle 10g构建。这项研究表明FreeSpan和PrefikSpan产生相同的输出。但是,FreeSpan更适合博士。 Soetomo综合医院,因为处理时间更快。

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