Knowledge discovery in clinical databases based on rough set model

由于大量的临床数据是以电子方式存储的，从这些临床数据库中发现知识是医学Informatica中重要的研究领域之一。为此，我们开发了一个基于变精度粗糙集模型的知识发现和机器学习研究实验系统KDD-R（a system for knowledge Discovery in Databases using Rough sets）。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关系并减少虚假数据。第三，系统从简化的数据库中计算规则。第四，评价决策。将该系统应用于脑膜炎脑病的临床数据库进行评价，结果表明，该系统获得了一些新的发现。临床数据库中的知识发现是医学信息学的一个重要研究领域。大部分的医学数据，如病历、实验室数据，都是以电子方式存储的，而临床数据库的数量将非常庞大，以至于即使是医学专家也无法处理如此庞大的数据库。因此，基于计算机的方法有望解决这一困难的情况。本文介绍了一个基于变精度粗糙集模型的数据库知识发现系统KDD-R。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关系并减少虚假数据。第三，系统从简化的数据库中计算规则。最后，对KDD-R在脑膜脑炎临床数据库中的应用进行了评价，结果表明，KDD-R在临床数据库中获得了一些新的发现。

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 Informatica. 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 in to 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 meninigenecephalitis, whose computational results show that everal new findings are obtained.Knowledge discovery in clinical databases is an important research area in medical informatics. Most of medical data, such as patient records, laboratory data, are now being stored electronically, and the amount of clinical databases will be too huge, so that even medical experts cannnot deal with such large databases. Thus, a computer-based approach is promising to solve this difficult situation. In this study, we introduce a system KDD-R (a system for Knowledge Discovery in Databases using Rough sets), based on Variable Precision Rough Set (VPRS) model.This system works as follows. 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, we apply KDD-R to a clinical database of meningoencephalitis, whose computational results show that several new findings are obtained from clinical databases.