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Title: Integration of Temporal abstraction and Dynamic Bayesian Networks for Coronary Heart Diagnosis
Authors: Orphanou, Kalia 
Stassopoulou, Athena 
Keravnou-Papailiou, Elpida 
Keywords: Coronary heart disease;Dynamic Bayesian networks;Medical diagnostic models;Temporal abstraction;Temporal reasoning
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: 1-Jan-2014
Publisher: IOS Press
Source: 7th European Starting AI Researcher Symposium, STAIRS 2014; Prague; Czech Republic; 18 August 2014 through 19 August 2014
metadata.dc.doi: 10.3233/978-1-61499-421-3-201
Abstract: Temporal data abstraction (TA) is a set of techniques aiming to abstract time-points into higher-level interval concepts and to detect significant trends in both low-level data and abstract concepts. Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal processes, temporal relationships between events and state changes through time. In this paper, we propose the integration of TA methods with DBNs in the context of medical decision-support systems, by presenting an extended DBN model. More specifically, we demonstrate the derivation of temporal abstractions which are used for building the network structure. We also apply machine learning algorithms to learn the parameters of the model through data. The model is applied for diagnosis of coronary heart disease using as testbed a longitudinal dataset. The classification accuracy of our model evaluated using the evaluation metrics of Precision, Recall and F1-score, shows the effectiveness of our proposed system.
ISSN: 09226389
Rights: © 2014 The Authors and IOS Press.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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