Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study
Cardiovascular disease (CVD) is one of the key causes for death worldwide. We consider the problem of modeling an imaging biomarker, Coronary Artery Calcification (CAC) measured by computed tomography, based on behavioral data. We employ the formalism of Dynamic Bayesian Network (DBN) and learn a DBN from these data. Our learned DBN provides insights about the associations of specific risk factors with CAC levels. Exhaustive empirical results demonstrate that the proposed learning method yields reasonable performance during cross-validation.
KeywordsBayesian Information Criterion Dynamic Bayesian Network Dynamic Bayesian Network Model Modeling Coronary Artery Propose Learning Method
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