Elpida Keravnou-Papailiou

Elpida Keravnou-Papailiou

Elpida Keravnou-Papailiou received the B.Tech. degree in Computer Science and the Ph.D. degree in Cybernetics from Brunel University, West London, in 1982 and 1985, respectively.

She is the first Rector of the Cyprus University of Technology, assuming her duties on January 4th 2012. She started her academic career from the Department of Computer Science, University College London as a Lecturer in 1985–1992, a Senior Lecturer 1991–1992, and the Director of the M.Sc. course in computer science. In 1992, she took up an academic position in the Department of Computer Science, University of Cyprus as an Associate Professor from 1992 to 1996, and a Professor since 1996. At the University of Cyprus, she served as a Vice-Rector for Academic Affairs (2002–2006), as the Dean of the School of Pure and Applied Sciences (1999–2002), and as the first Chairperson of the Department of Computer Science (1994–1998). She also served as the President of the Governing Board of the Cyprus University of Technology (2009–2010). She is currently the Vice-Chair of the Evaluation Committee for Private Universities in Cyprus and a Member of the Governing Board of the European Institute of Innovation and Technology and its Executive Committee (www.eit.europa.eu). She has carried out research in the areas of knowledge engineering, expert systems, deep knowledge models, diagnostic reasoning, temporal reasoning, artificial intelligence in medicine, intelligent data analysis in medicine and hybrid decision support systems. She is an Associate Editor of the scientific journal Artificial Intelligence in Medicine (Elsevier) since the launch of the journal in 1989. During the period 2003–2005, she served as the Chairperson of the Artificial Intelligence in Medicine Europe Board.


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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