Integration of Temporal abstraction and Dynamic Bayesian Networks for Coronary Heart Diagnosis

Kalia Orphanou, Athena Stassopoulou, Elpida Keravnou

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

Abstract

Temporal data abstraction (TA) is a set of techniques aiming to abstract time-points into higher-level interval concepts and to detect significant trends in both low-level data and abstract concepts. Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal processes, temporal relationships between events and state changes through time. In this paper, we propose the integration of TA methods with DBNs in the context of medical decision-support systems, by presenting an extended DBN model. More specifically, we demonstrate the derivation of temporal abstractions which are used for building the network structure. We also apply machine learning algorithms to learn the parameters of the model through data. The model is applied for diagnosis of coronary heart disease using as testbed a longitudinal dataset. The classification accuracy of our model evaluated using the evaluation metrics of Precision, Recall and F1-score, shows the effectiveness of our proposed system.

Original languageEnglish
Title of host publicationSTAIRS 2014 - Proceedings of the 7th European Starting AI Researcher Symposium
PublisherIOS Press
Pages201-210
Number of pages10
Volume264
ISBN (Electronic)9781614994206
DOIs
Publication statusPublished - 2014
Event7th European Starting AI Researcher Symposium, STAIRS 2014 - Prague, Czech Republic
Duration: 18 Aug 201419 Aug 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume264
ISSN (Print)0922-6389

Other

Other7th European Starting AI Researcher Symposium, STAIRS 2014
CountryCzech Republic
CityPrague
Period18/08/1419/08/14

Keywords

  • coronary heart disease
  • Dynamic Bayesian networks
  • medical diagnostic models
  • temporal abstraction
  • temporal reasoning

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  • Cite this

    Orphanou, K., Stassopoulou, A., & Keravnou, E. (2014). Integration of Temporal abstraction and Dynamic Bayesian Networks for Coronary Heart Diagnosis. In STAIRS 2014 - Proceedings of the 7th European Starting AI Researcher Symposium (Vol. 264, pp. 201-210). (Frontiers in Artificial Intelligence and Applications; Vol. 264). IOS Press. https://doi.org/10.3233/978-1-61499-421-3-201