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Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study

  • Shuo YangEmail author
  • Kristian Kersting
  • Greg Terry
  • Jefferey Carr
  • Sriraam Natarajan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9105)

Abstract

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.

Keywords

Bayesian Information Criterion Dynamic Bayesian Network Dynamic Bayesian Network Model Modeling Coronary Artery Propose Learning Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shuo Yang
    • 1
    Email author
  • Kristian Kersting
    • 2
  • Greg Terry
    • 3
  • Jefferey Carr
    • 3
  • Sriraam Natarajan
    • 1
  1. 1.Indiana UniversityBloomingtonUSA
  2. 2.TU DortmundDortmundGermany
  3. 3.Vanderbilt UniversityNashvilleUSA

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