INFORMATION GAIN OF STRUCTURED MEDICAL DIAGNOSTIC TESTS - Integration of Bayesian Networks and Ontologies

摘要

Usage of Bayesian networks in medical decision support system is in general case twofold: (1) for obtaining probabilities of occurrence of medical events (i.e. possible diagnosis) and (2) for obtaining information gain of actions that can be taken (i.e. diagnostic tests). On the other hand, typical role of ontology is to provide a framework for definition of medical concepts, their structure and relations among them. In medical practice diagnostic tests are commonly comprised of number of measurements or sub-tests - a structure which is straightforwardly described by ontological language. In this paper we are analyzing the information gain of such structured medical diagnostic tests. The purpose of this analysis is to allow finding (1) which structured medical diagnostic test is at the given point the most informative one and (2) which elementary measurements within a given diagnostic test are the most informative ones. Furthermore, we are analyzing some computational issues which arise in the reasoning process.