TY - GEN
T1 - Integration of Temporal abstraction and Dynamic Bayesian Networks for Coronary Heart Diagnosis
AU - Orphanou, Kalia
AU - Stassopoulou, Athena
AU - Keravnou, Elpida
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - coronary heart disease
KW - Dynamic Bayesian networks
KW - medical diagnostic models
KW - temporal abstraction
KW - temporal reasoning
UR - http://www.scopus.com/inward/record.url?scp=84948671838&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-421-3-201
DO - 10.3233/978-1-61499-421-3-201
M3 - Conference contribution
AN - SCOPUS:84948671838
VL - 264
T3 - Frontiers in Artificial Intelligence and Applications
SP - 201
EP - 210
BT - STAIRS 2014 - Proceedings of the 7th European Starting AI Researcher Symposium
PB - IOS Press
T2 - 7th European Starting AI Researcher Symposium, STAIRS 2014
Y2 - 18 August 2014 through 19 August 2014
ER -