George Stalidis

Medical support system for continuation of care based on XML web technology

In this paper, the implementation of an Internet-based telematic service for medical support is presented, which was developed and operated in pilot form within the INTRANET HEALTH CLINIC project--a 2-year project supported by the European Commission under the Health Telematics Programme. The aim of the application is to offer high quality care to users of health services over inexpensive communication pathways, using Internet-based, interactive communication tools, like remote access to medical records and transmission of multimedia information. The XML technology was employed to achieve customised views on patient data, according to the access rights of different user profiles. Strict security and access control policy were implemented to ensure secure transmission of medical data through the Internet. The system was designed to collaborate with existing clinical patient record systems and to be adjustable to different medical applications. Current implementations include the fields of Oncology, Lupus Erythrematosis, Obstetrics and Chronic Obstructive Pulmonary disease. The results of the pilot operation with oncological patients in Greece were encouraging, so that the refining of the system and its expansion to a large number of patients is already in progress.

Model-based processing scheme for quantitative 4-D cardiac MRI analysis

Presents an integrated model-based processing scheme for cardiac magnetic resonance imaging (MRI), embedded in an interactive computing environment suitable for quantitative cardiac analysis, which provides a set of functions for the extraction, modeling, and visualization of cardiac shape and deformation. The methods apply 4-D processing (three spatial and one temporal) to multiphase multislice MRI acquisitions and produce a continuous 4-D model of the myocardial surface deformation. The model is used to measure diagnostically useful parameters, such as wall motion, myocardial thickening, and myocardial mass measurements. The proposed model-based shape extraction method has the advantage of integrating local information into an overall representation and produces a robust description of cardiac cavities. A learning segmentation process that incorporates a generating-shrinking neural network is combined with a spatiotemporal parametric modeling method through functional basis decomposition. A multiscale approach is adopted, which uses at each step a coarse-scale model defined at the previous step in order to constrain the boundary detection. The main advantages of the proposed methods are efficiency, lack of uncertainty about convergence, and robustness to image artifacts.

Knowledge engineering for adverse drug event prevention

The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards implementation, performance and validation aspects that highlight its applicability and virtue in medication safety.

Detection and modeling of infarcted myocardium regions in MRI images using a contour deformable model

This work provides a semi-automatic method for defining and modeling of the infarcted myocardial tissue in MRI images. A deformable contour model based on Fourier decomposition is used to define the border of the infarcted region in successive slice images. A new fast algorithm has been developed for fitting the curve to the borders of the region. The method includes a two-stage process which detects boundary points and directly calculates the model parameters. The method has been tested on MR images from patients with myocardial infarction. Results show that the infarcted region modeling method performs well, being fast, accurate and relatively insensitive to image noise and inhomogeneities.

Modeling of cardiac motion using wavelets: comparison with Fourier-based models

In this paper, a 4-D parametric model based on wavelets is applied to the representation of the shape and motion of myocardial surfaces. It is qualitatively, and quantitatively evaluated on multi-phase cardiac MRI data and compared with the previously presented 4-D Fourier-based model. The wavelet-based model uses a number of boundary points, extracted from the imaging data, as samples on which a moving 3-D surface is fitted. The resulting model is continuous and smooth and provides a full description of cardiac motion in 3-D, from which measurements for quantitative motion analysis can be derived. Application results showed that the localized and multi-resolution nature of wavelets allowed more accurate representation of cardiac shape and motion than the Fourier model, especially regarding shape details and fast deformation, while the Fourier model provided a smoother surface, more robust to noisy data.

Parametric 4D modeling of myocardial surface motion applied to MRI data

A deformable 4D model based on Fourier decomposition is presented which was successfully applied to the modeling of the cardiac endocardial and epicardial surfaces and their deformation in time. The proposed method automatically selects boundary points on the myocardial surfaces in the 3D space, collecting a different set of points for each phase. A 4D model is then fitted to the selected points by calculating its parameters directly using an FFT algorithm. The constructed model provides a continuous and smooth representation of the moving surfaces which is consistent with the registration between different phases. Testing on 3D multi-phase MRI data and on 2D multi-slice multi-phase data has provided satisfactory results.

Application of a 3-D ischemic heart model derived from MRI data to the simulation of the electrical activity of the heart

The propagation of the electrical activity of the heart is simulated over regions containing infarcted tissue. The method is applied to patients suffering from ischemia, in order to study the impact of the injury on the electrical function of the heart. The spatial distribution of the infarcted tissue and the shape of the endocardial and the epicardial surfaces are derived from MRI data using a semiautomatic method based on 3D Fourier parametric modeling. A 2D grid is then constructed which represents a selected part of the epicardial surface and contains information about the condition of the tissue. This grid is used as input to the simulation algorithm which estimates the ionic currents over time and the propagation of the electric impulse.

Understanding Xenophobia in Greece: A Correspondence Analysis

Studies of xenophobia have focused either on socio-economic context that accentuates xenophobic attitudes or on perceptions of immigrants, namely symbolic and realistic threats as well as on social distance from immigrants. This study examines closely the relationship among various components of xenophobia and their contribution in the formation of particular xenophobic groups. The analysis identified four different xenophobic groups, i.e. a) The distant xenophobic group , b) The core xenophobic group , c) The subtle xenophobic group and d) The ambivalent xenophobic group . The groups’ profiles are synthesized through negative, neutral and positive properties of overall attitudes towards immigrants, perceived threats, political xenophobia, social distance, authoritarian attitudes and individual social characteristics. The survey results demonstrate that a multidimensional conceptualization of xenophobia is needed both at the level of objective social condition and of individual and collective perceptions.

Using learning classification and four-dimensional parametric modeling for the analysis of myocardial thickening

A previously, presented 4-D method for modeling the myocardial surfaces and their deformation was refined and applied to the measurement of diagnostic parameters. The method initially defines the myocardial surfaces of the left ventricle. Based on the derived model, measurements of myocardial thickness and thickening in time were produced and used to construct 3-D myocardial thickness maps, which were color coded on the surface for visualization over time. Estimations of myocardial strain maps were also produced, taking into account the deformation of myocardial surfaces. The shape extraction method was improved by utilizing a learning segmentation process, based on a generating-shrinking neural network classifier. A multiscale approach was also adopted which starts from a rough approximation of the expected shape and gradually proceeds to the accurate model. The method was applied to multi-slice multi-phase MRI cardiac acquisitions. Although the displacement and strain maps were not derived from true functional data, have shown promise for cardiac function diagnosis.

Social class and mental distress in Greek urban communities during the period of economic recession

Background: This study draws on old and well-established evidence that economic change, and especially recession, affects people’s lives, behavior and mental health. Even though the literature is rich on the relationship between unemployment and mental distress, there is a renewed research interest on the link between socio-economic inequalities and psychological health. Aims: The study investigates the relationship of social class with mental distress during the hard times of persistent and severe economic crisis in Greece by conducting a comparative, community study in the country’s second largest city, Thessaloniki. Method: A face-to-face structured interview covering living conditions, life events, chronic stressors and coping strategies was employed to 300 residents of socio-economically contrasting neighborhood areas. Social class was operationalized by Erik Olin Wright’s social class position typology, based on ownership and control over productive assets. The method of multiple correspondence analysis (MCA) was also applied to analyze the collected data. Results: The results indicated that mental distress was significantly differentiated across social classes and in each residential area. Unemployed and unskilled workers were the most vulnerable groups in terms of psychological health. Chronic stress arose in this study as a risk factor for poor mental health outcomes and it was associated to low marital quality, intense economic burden and impoverished housing conditions. Conclusion: Those who face income loss, job loss and disability are at high risk for poverty and marginalization, suffering from greater psychological distress.