Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system
This addresses the need for reliable testing of medical DSS before clinical use, though it is incremental as it adapts an existing algorithm to a specific domain.
The authors tackled the problem of systematically testing clinical decision support systems (DSS) to ensure they conform to clinical guidelines, by developing an exhaustive dynamic verification method using the C4.5 algorithm to build decision trees from DSS inputs and outputs, and successfully applied it to a diabetes DSS.
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.