Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/10006
Title: DBN-extended: A dynamic Bayesian network model extended with temporal abstractions for coronary heart disease prognosis
Authors: Orphanou, Kalia 
Stassopoulou, Athena 
Keravnou-Papailiou, Elpida 
Keywords: Medical prognostic models;Temporal reasoning;Coronary heart disease;Dynamic Bayesian networks;Temporal abstraction
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: 1-May-2016
Publisher: IEEE
Source: Journal of Biomedical and Health Informatics, 2016, Volume 20, Issue 3, Article number 7080845, Pages 944-952
metadata.dc.doi: 10.1109/JBHI.2015.2420534
Abstract: Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal events and their causal and temporal dependencies. Temporal abstraction (TA) is a knowledge-based process that abstracts raw temporal data into higher level interval-based concepts. In this paper, we present an extended DBN model that integrates TA methods with DBNs applied for prognosis of the risk for coronary heart disease. More specifically, we demonstrate the derivation of TAs from data, which are used for building the network structure. We use machine learning algorithms to learn the parameters of the model through data. We apply the extended model to a longitudinal medical dataset and compare its performance to the performance of a DBN implemented without TAs. The results we obtain demonstrate the predictive accuracy of our model and the effectiveness of our proposed approach.
URI: http://ktisis.cut.ac.cy/handle/10488/10006
ISSN: 21682194
Rights: © 2013 IEEE.
Type: Article
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