Authors:
Marin Prcela
1
;
Dragan Gamberger
1
;
Tomislav Šmuc
1
and
Nikola Bogunović
2
Affiliations:
1
Rudjer Boskovic Institute, Croatia
;
2
University of Zagreb, Croatia
Keyword(s):
Knowledge representation, Ontologies, Bayesian networks, Integration, Information gain, Decision support system, Expert system.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Expert Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Support for Clinical Decision-Making
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
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 ar
ise in the reasoning process.
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