Athena Stassopoulou

Athena Stassopoulou

Athena Stassopoulou received the B.Sc. degree in Computer Science and Mathematics (joint Hons.) from the University of Manchester, Manchester, U.K. in 1992 and the Ph.D. degree in Artificial Intelligence, from the University of Surrey, Centre for Vision Speech and Signal Processing, U.K., in 1996. She is a Professor of Computer Science at the University of Nicosia where she served as the founding Head of the CS Department (2001–2008).

She worked as a Postdoctoral Researcher and as a Senior Research Associate at the Center for Mapping, The Ohio State University, USA. She has more than 20 years of research experience and has worked in projects funded by NASA (Image Understanding Initiative), the National Imagery and Mapping Agency in the USA, the European Union and the Research Promotion Foundation of Cyprus. She has been publishing in international refereed journals and conferences in the following areas which constitute her research interests: uncertain reasoning, Bayesian networks, geographic information systems, computer vision and image understanding, machine learning, neural networks and more recently, in artificial intelligence applications for the web.


Contributions

  • DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis
    DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis

    Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal events and their causal and temporal dependencies. Temporal abstraction (TA) is a knowledge-based process that abstracts raw temporal data into higher level interval-based concepts. In this paper, we present an extended DBN model that integrates TA methods with DBNs applied for prognosis of the risk for coronary heart disease. More specifically, we demonstrate the derivation of TAs from data, which are used for building the network structure. We use machine learning algorithms to learn the parameters of the model through data. We apply the extended model to a longitudinal medical dataset and compare its performance to the performance of a DBN implemented without TAs. The results we obtain demonstrate the predictive accuracy of our model and the effectiveness of our proposed approach.

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