Use of the C4.5 machine learning algorithm to test a clinical
  guideline-based decision support system
  Well-designed medical decision support system (DSS) have been shown to
improve health care quality. However, before they can be used in real clinical
situations, these systems must be extensively tested, to ensure that they
conform to the clinical guidelines (CG) on which they are based. Existing
methods cannot be used for the systematic testing of all possible test cases.
We describe here a new exhaustive dynamic verification method. In this method,
the DSS is considered to be a black box, and the Quinlan C4.5 algorithm is used
to build a decision tree from an exhaustive set of DSS input vectors and
outputs. This method was successfully used for the testing of a medical DSS
relating to chronic diseases: the ASTI critiquing module for type 2 diabetes.
