Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system

Jean-Baptiste Lamy; Anis Ellini; Vahid Ebrahiminia; Jean-Daniel Zucker; Hector Falcoff; Alain Venot

Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system

Artificial Intelligence 2013-12-04 v1

Authors: Jean-Baptiste Lamy , Anis Ellini , Vahid Ebrahiminia , Jean-Daniel Zucker , Hector Falcoff , Alain Venot

Abstract

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.

Keywords

clinical prediction medical diagnosis fault tree analysis

Cite

@article{arxiv.1312.0735,
  title  = {Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system},
  author = {Jean-Baptiste Lamy and Anis Ellini and Vahid Ebrahiminia and Jean-Daniel Zucker and Hector Falcoff and Alain Venot},
  journal= {arXiv preprint arXiv:1312.0735},
  year   = {2013}
}

Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system

Abstract

Keywords

Cite

Related papers