Selection of Relevant Rules Within Clinical Decision Support

Clinical decision support systems represent important telemedicine tools with the ability to help physicians within the decision process leading to determining diagnosis, therapy or prognosis of patients. We proposed and implemented a prototype of a clinical decision support system, which has the form of an internet classification service. A specific property of this system is a sophisticated statistical component, which allows to handle also a large number of symptoms and signs. It namely optimizes the selection of such symptoms and signs which are the most relevant for determining the diagnosis. The performance of the prototype was verified on an analysis of gene expression data from a cardiovascular genetic study. The paper discusses principles of multivariate statistical thinking and reveals challenges of analyzing high-dimensional data with the number of observed variables (symptoms and signs) largely exceeding the number of observations (patients)