A distributed clinical decision support system applied to prostate cancer diagnosis

Abstract

Currently, the best way to reduce the mortality of cancer is to detect it and treat it in the earliest stages. Automatic decision support systems are very helpful in this task but their performance is constrained by different factors and sometimes it is difficult to find a method with high sensitivity and specificity rates. One solution to this problem can be the collaboration between independent decision support systems. This article presents a proposal for a distributed and collaborative prostate cancer automatic diagnosis system based on artificial neural networks, which pretends to increase the accuracy of the decision support system combining the independent contributions of different artificial diagnosis entities. © 2010 Springer-Verlag Berlin Heidelberg.