Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers.
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.
Lee
Sun-Mi
SM
School of Nursing, University of Maryland at Baltimore, 655 W. Lombard, Baltimore, MD, USA. Sun-mi@son.umaryland.edu
Abbott
Patricia A
PA
D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology; D016208:Databases, Factual; D006801:Humans; D015233:Models, Statistical; D015399:Nursing Research