COMPUTATIONAL INTELLIGENCE FOR LABORATORY INFORMATION-SYSTEMS

Non-linear models, such as given by neural networks and fuzzy logic, have established a good reputation for medical data analysis as computational and logical counterparts to statistical methods. Whereas multilayer perceptrons perform well with large data sets, a combination of neural learning together with fuzzy logical network interpretations provides a network reduction well suited for smaller data sets. The aim of this paper is to present an approach to neural fuzzy systems data analysis and knowledge acquisition in laboratory information systems. We also describe a software system, DiagaiD, which provides an analysis and development workbench involving laboratory data.