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Extracting drug utilization knowledge using self-organizing map and rough set theory

机译:使用自组织图和粗糙集理论提取药物利用知识

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Cardiovascular disease is becoming the major cause of death in many industrialized countries. People who receive long-term treatments usually ignore the progress of the disease states. Therefore, it is critical and necessary to evaluate drug utilization and laboratory test in order to discover the knowledge that is beneath and can be extracted from those raw data. This paper utilizes techniques of self-organizing map (SOM) and rough set theory (RST) to discover the trend of individual patient's condition. With 10-fold cross-verification, the proposed SOM-SOM-RST process successfully and effectively detects patients whose diagnosis codes have been changed during the period of investigation and attains an accuracy of approximate 98%. This method can remind physicians to reevaluate the disease conditions of their patients.
机译:在许多工业化国家中,心血管疾病正成为主要的死亡原因。接受长期治疗的人通常会忽略疾病状态的进展。因此,评估药物利用率和实验室测试以发现底层知识并从这些原始数据中提取知识是至关重要且必要的。本文利用自组织图(SOM)和粗糙集理论(RST)的技术来发现个别患者病情的趋势。通过10倍交叉验证,所提出的SOM-SOM-RST过程成功并有效地检测了在调查期间诊断代码已更改的患者,并达到了约98%的准确度。这种方法可以提醒医生重新评估患者的疾病状况。

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