The application of data mining techniques upon medical data is certainly beneficial for researchers interested in discerning the com lexity of healthcare rocesses in real-life operational situations.In this paper we resent a methodology,together with its computational implementation,for the automated extraction of data-defining CNF symbolic rules from medical data-sets comprising both annotated and un-annotated attributes.We ropose a hybrid approach for symbolic rule extraction which features a sequence of methods including data clustering,data discretization and eventually symbolic rule discovery via rough set approximation.We present a generic data mining workbench that can generate cluster/class- defining symbolic rules from medical data,such that the resultant symbolic rules are directly a licable to medical rule-based expert systems.