Risk assessment for primary coronary heart disease event using dynamic bayesian networks

Kalia Orphanou, Athena Stassopoulou, Elpida Keravnou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Coronary heart disease (CHD) is the leading cause of mortality worldwide. Primary prevention ofCHDdenotes limiting a firstCHDevent in individuals who have not been formally diagnosed with the disease. This paper demonstrates how the integration of a Dynamic Bayesian network (DBN) and temporal abstractions (TAs) can be used for assessing the risk of a primaryCHDevent. More specifically, we introduce basic TAs into the DBN nodes and apply the extended model to a longitudinal CHDdataset for risk assesment. The obtained results demonstrate the effectiveness of our proposed approach.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings
PublisherSpringer Verlag
Pages161-165
Number of pages5
Volume9105
ISBN (Print)9783319195506
DOIs
Publication statusPublished - 2015
Event15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy
Duration: 17 Jun 201520 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9105
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th Conference on Artificial Intelligence in Medicine, AIME 2015
CountryItaly
CityPavia
Period17/06/1520/06/15

Keywords

  • Dynamic Bayesian networks
  • Primary coronary heart disease
  • Risk assessment
  • Temporal abstraction
  • Temporal reasoning

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  • Cite this

    Orphanou, K., Stassopoulou, A., & Keravnou, E. (2015). Risk assessment for primary coronary heart disease event using dynamic bayesian networks. In Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings (Vol. 9105, pp. 161-165). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105). Springer Verlag. https://doi.org/10.1007/978-3-319-19551-3_20