Evangelos Kaimakamis

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In this paper, an ontology-based system (KnowBaSICS-M) is presented for the semantic management of Medical Computational Problems (MCPs), i.e., medical problems and computerised algorithmic solutions. The system provides an open environment, which: (1) allows clinicians and researchers to retrieve potential algorithmic solutions pertinent to a medical(More)
This paper presents a semantic rule-based system for the composition of successful algorithmic pathways capable of solving medical computational problems (MCPs). A subset of medical algorithms referring to MCP solving concerns well-known medical problems and their computational algorithmic solutions. These solutions result from computations within(More)
Recently, a great interest has emerged in e-learning approaches for medical education. In particular, Problem/Case based learning constitutes a significant initiative in the domain. In this paper, we propose an ontology-based approach to constructing medical computational problems (MCPs) to be used in electronic medical education. Specifically, we elaborate(More)
In recent years, there has been a major advance in the treatment of pulmonary hypertension. New medications are continually added to the therapeutic arsenal. The prostanoids are among the first agents used to treat pulmonary hypertension and are currently considered the most effective. This case study describes a 63-year-old man who was diagnosed with(More)
INTRODUCTION Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep,(More)
OBJECTIVES To ascertain the stakeholders' views and devise recommendations for further stages of the Wearable Sensing and Smart Cloud Computing for Integrated Care to Chronic Obstructive Pulmonary Disease (COPD) Patients with Co-morbidities (WELCOME) system development. This system aims to create a wearable vest to monitor physiological signals for patients(More)
In this work thirty features were tested in order to identify the best feature set for the robust detection of wheezes. The features include the detection of the wheezes signature in the spectrogram space (WS-SS) and twenty-nine musical features usually used in the context of Music Information Retrieval. The method proposed to detect the signature of(More)
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