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Comput Methods Programs Biomed. 2013 Dec;112(3):580-98. doi: 10.1016/j.cmpb.2013.07.008. Epub 2013 Aug 15.

Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support.

Author information

1
Department of Computer Engineering, Technological Educational Institute of Central Greece, 3rd Km Old National Road Lamia-Athens, 35100 Lamia, Greece. Electronic address: epapageorgiou@teilam.gr.

Abstract

This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models' performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics' suggestion for UTI.

KEYWORDS:

Bayesian networks; Decision support; Fuzzy cognitive maps; Knowledge representation

PMID:
23953959
DOI:
10.1016/j.cmpb.2013.07.008
[Indexed for MEDLINE]

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