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.
Lamy
Jean-Baptiste
JB
Laboratoire d'Informatique Médicale et de Bio-informatique (LIM&BIO), UFR SMBH, Université Paris 13, Bobigny, France. jibalamy@free.fr
Ellini
Anis
A
Ebrahiminia
Vahid
V
Zucker
Jean-Daniel
JD
Falcoff
Hector
H
Venot
Alain
A
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy; D005103:Expert Systems; D019983:Guideline Adherence; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D051188:Knowledge Bases; D016347:Medical Records Systems, Computerized; D017410:Practice Guidelines as Topic
D007004:Hypoglycemic Agents