Tsumoto S, Ziarko W, Shan N, Tanaka H
Department of Information Medicine, Tokyo Medical and Dental University, Japan.
Proc Annu Symp Comput Appl Med Care. 1995:270-4.
Since a large amount of clinical data are being stored electronically, discovery of knowledge from such clinical databases is one of the important growing research area in medical informatics. For this purpose, we develop KDD-R (a system for Knowledge Discovery in Databases using Rough sets), an experimental system for knowledge discovery and machine learning research using variable precision rough sets (VPRS) model, which is an extension of original rough set model. This system works in the following steps. First, it preprocesses databases and translates continuous data into discretized ones. Second, KDD-R checks dependencies between attributes and reduces spurious data. Third, the system computes rules from reduced databases. Finally, fourth, it evaluates decision making. For evaluation, this system is applied to a clinical database of meningoencephalitis, whose computational results show that several new findings are obtained.
由于大量临床数据正以电子方式存储，从这类临床数据库中发现知识是医学信息学中一个重要的新兴研究领域。为此，我们开发了KDD - R（一种使用粗糙集进行数据库知识发现的系统），这是一个使用可变精度粗糙集（VPRS）模型进行知识发现和机器学习研究的实验系统，VPRS模型是原始粗糙集模型的扩展。该系统按以下步骤工作。首先，它对数据库进行预处理并将连续数据转换为离散数据。其次，KDD - R检查属性之间的依赖性并减少虚假数据。第三，系统从精简后的数据库中计算规则。最后，第四步，它评估决策制定。为了进行评估，该系统被应用于一个脑膜脑炎的临床数据库，其计算结果表明获得了一些新发现。
