Lee Sun-Mi, Abbott Patricia A
School of Nursing, University of Maryland at Baltimore, 655 W. Lombard, Baltimore, MD, USA.
J Biomed Inform. 2003 Aug-Oct;36(4-5):389-99. doi: 10.1016/j.jbi.2003.09.022.
The growth of nursing databases necessitates new approaches to data analyses. These databases, which are known to be massive and multidimensional, easily exceed the capabilities of both human cognition and traditional analytical approaches. One innovative approach, knowledge discovery in large databases (KDD), allows investigators to analyze very large data sets more comprehensively in an automatic or a semi-automatic manner. Among KDD techniques, Bayesian networks, a state-of-the art representation of probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks allow investigators to combine domain knowledge with statistical data, enabling nurse researchers to incorporate clinical and theoretical knowledge into the process of knowledge discovery in large datasets. This tailored discussion presents the basic concepts of Bayesian networks and their use as knowledge discovery tools for nurse researchers.
护理数据库的增长需要新的数据分析方法。这些数据库规模庞大且具有多维度特点，很容易超出人类认知能力和传统分析方法的处理范围。一种创新方法，即大型数据库中的知识发现（KDD），使研究人员能够以自动或半自动方式更全面地分析非常大的数据集。在KDD技术中，贝叶斯网络作为一种通过图形表示概率知识的先进方法，近年来已成为医疗保健领域模式识别和分类的关键。与一些数据挖掘技术不同，贝叶斯网络允许研究人员将领域知识与统计数据相结合，使护士研究人员能够将临床和理论知识纳入大型数据集中的知识发现过程。本次专题讨论介绍了贝叶斯网络的基本概念及其作为护士研究人员知识发现工具的应用。
