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

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Artificial Intelligence in Medicine (AIME 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9105))

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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.

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Authors and Affiliations

  1. Indiana University, Bloomington, USA

    Shuo Yang & Sriraam Natarajan

  2. TU Dortmund, Dortmund, Germany

    Kristian Kersting

  3. Vanderbilt University, Nashville, USA

    Greg Terry & Jefferey Carr

Authors
  1. Shuo Yang
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  2. Kristian Kersting
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  3. Greg Terry
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  4. Jefferey Carr
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  5. Sriraam Natarajan
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Corresponding author

Correspondence to Shuo Yang .

Editor information

Editors and Affiliations

  1. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    John H. Holmes

  2. University of Pavia, Pavia, Italy

    Riccardo Bellazzi

  3. University of Pavia, Pavia, Italy

    Lucia Sacchi

  4. University of Manchester, Manchester, United Kingdom

    Niels Peek

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© 2015 Springer International Publishing Switzerland

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Yang, S., Kersting, K., Terry, G., Carr, J., Natarajan, S. (2015). Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study. In: Holmes, J., Bellazzi, R., Sacchi, L., Peek, N. (eds) Artificial Intelligence in Medicine. AIME 2015. Lecture Notes in Computer Science(), vol 9105. Springer, Cham. https://doi.org/10.1007/978-3-319-19551-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-19551-3_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19550-6

  • Online ISBN: 978-3-319-19551-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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