Abstract
Cardiovascular disease (CVD) is one of the key causes for death worldwide. We consider the problem of modeling an imaging biomarker, CoronaryArtery 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.
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CITATION STYLE
Yang, S., Kersting, K., Terry, G., Carr, J., & Natarajan, S. (2015). Modeling coronary artery calcification levels from behavioral data in a clinical study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9105, pp. 182–187). Springer Verlag. https://doi.org/10.1007/978-3-319-19551-3_24
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