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

Kalia Orphanou; Athena Stassopoulou; Elpida Keravnou

doi:10.1007/978-3-319-19551-3_20

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

Kalia Orphanou, Athena Stassopoulou, Elpida Keravnou

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings
Publisher	Springer Verlag
Pages	161-165
Number of pages	5
Volume	9105
ISBN (Print)	9783319195506
DOIs	https://doi.org/10.1007/978-3-319-19551-3_20
Publication status	Published - 2015
Event	15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy Duration: 17 Jun 2015 → 20 Jun 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9105
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	15th Conference on Artificial Intelligence in Medicine, AIME 2015
Country/Territory	Italy
City	Pavia
Period	17/06/15 → 20/06/15

Keywords

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

Access to Document

10.1007/978-3-319-19551-3_20

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

Orphanou, Kalia ; Stassopoulou, Athena ; Keravnou, Elpida. / Risk assessment for primary coronary heart disease event using dynamic bayesian networks. Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings. Vol. 9105 Springer Verlag, 2015. pp. 161-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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author = "Kalia Orphanou and Athena Stassopoulou and Elpida Keravnou",

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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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9105, Springer Verlag, pp. 161-165, 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, 17/06/15. https://doi.org/10.1007/978-3-319-19551-3_20

Risk assessment for primary coronary heart disease event using dynamic bayesian networks. / Orphanou, Kalia; Stassopoulou, Athena; Keravnou, Elpida.
Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings. Vol. 9105 Springer Verlag, 2015. p. 161-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Keravnou, Elpida

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N2 - 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.

AB - 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.

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Orphanou K, Stassopoulou A, Keravnou E. 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. Springer Verlag. 2015. p. 161-165. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-19551-3_20

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

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