DBN-extended: A Dynamic Bayesian network model extended with temporal abstractions for coronary heart disease prognosis
Date
2015ISSN
2168-2208Source
IEEE Journal Of Biomedical And Health InformaticsGoogle Scholar check
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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 which abstracts raw temporal data into higher level interval-based concepts. In this paper, we present an extended DBN model which integrates TA methods with DBNs applied for prognosis of the risk for coronary heart disease (CHD). More specifically, we demonstrate the derivation of temporal abstractions 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 temporal abstractions. The results we obtain demonstrate the predictive accuracy of our model and the effectiveness of our proposed approach.