Dimitrios D. Koutsouris

Using key performance indicators as knowledge-management tools at a regional health-care authority level

The advantages of the introduction of information and communication technologies in the complex health-care sector are already well-known and well-stated in the past. It is, nevertheless, paradoxical that although the medical community has embraced with satisfaction most of the technological discoveries allowing the improvement in patient care, this has not happened when talking about health-care informatics. Taking the above issue of concern, our work proposes an information model for knowledge management (KM) based upon the use of key performance indicators (KPIs) in health-care systems. Based upon the use of the balanced scorecard (BSC) framework (Kaplan/Norton) and quality assurance techniques in health care (Donabedian), this paper is proposing a patient journey centered approach that drives information flow at all levels of the day-to-day process of delivering effective and managed care, toward information assessment and knowledge discovery. In order to persuade health-care decision-makers to assess the added value of KM tools, those should be used to propose new performance measurement and performance management techniques at all levels of a health-care system. The proposed KPIs are forming a complete set of metrics that enable the performance management of a regional health-care system. In addition, the performance framework established is technically applied by the use of state-of-the-art KM tools such as data warehouses and business intelligence information systems. In that sense, the proposed infrastructure is, technologically speaking, an important KM tool that enables knowledge sharing amongst various health-care stakeholders and between different health-care groups. The use of BSC is an enabling framework toward a KM strategy in health care.

EEG epileptic seizure detection using k-means clustering and marginal spectrum based on ensemble empirical mode decomposition

The detection of epileptic seizures is of primary interest for the diagnosis of patients with epilepsy. Epileptic seizure is a phenomenon of rhythmicity discharge for either a focal area or the entire brain and this individual behavior usually lasts from seconds to minutes. The unpredictable and rare occurrences of epileptic seizures make the automated detection of them highly recommended especially in long term EEG recordings. The present work proposes an automated method to detect the epileptic seizures by using an unsupervised method based on k-means clustering end Ensemble Empirical Decomposition (EEMD). EEG segments are obtained from a publicly available dataset and are classified in two categories “seizure” and “non-seizure”. Using EEMD the Marginal Spectrum (MS) of each one of the EEG segments is calculated. The MS is then divided into equal intervals and the averages of these intervals are used as input features for k-Means clustering. The evaluation results are very promising indicating overall accuracy 98% and is comparable with other related studies. An advantage of this method that no training data are used due to the unsupervised nature of k-Means clustering.

Tele-monitoring and diagnostic for fall prevention: The KINOPTIM concept

Falls among the elderly is a major public health problem due to its causality for increased dependency. It can be tackled thanks to innovative Information and Communication Technologies (ICT) along with new forms of service delivery organization such as home care. In the context of a Marie Curie project we have proposed the KINOPTIM concept, a comprehensive fall prevention and holistic management service. This paper provides a review of this innovative concept starting with a brief introduction and the rationale behind its development, the description of the platform and service to be eventually piloted and closing with conclusions and perspectives section since it is still in an ongoing research phase.

An automatic electroencephalography blinking artefact detection and removal method based on template matching and ensemble empirical mode decomposition

Electrooculographic (EOG) artefacts are one of the most common causes of Electroencephalogram (EEG) distortion. In this paper, we propose a method for EOG Blinking Artefacts (BAs) detection and removal from EEG. Normalized Correlation Coefficient (NCC), based on a predetermined BA template library was used for detecting the BA. Ensemble Empirical Mode Decomposition (EEMD) was applied to the contaminated region and a statistical algorithm determined which Intrinsic Mode Functions (IMFs) correspond to the BA. The proposed method was applied in simulated EEG signals, which were contaminated with artificially created EOG BAs, increasing the Signal-to-Error Ratio (SER) of the EEG Contaminated Region (CR) by 35dB on average.

Integrating Legacy Medical Data Sensors in a Wireless Network Infrastucture

In the process of developing a wireless networking solution to provide effective field-deployable communications and telemetry support for rescuers during major natural disasters, we are faced with the task of interfacing the multitude of medical and other legacy data collection sensors to the network grid. In this paper, we detail a number of solutions, with particular attention given to the issue of data security. The chosen implementation allows for sensor control and management from remote network locations, while the sensors can wirelessly transmit their data to nearby network nodes securely, utilizing the latest commercially available cryptography solutions. Initial testing validates the design choices, while the network-enabled sensors are being integrated in the overall wireless network security framework

A multilayered architecture for the development of smart card-based healthcare applications

The use of smart cards in healthcare promises to improve both the quality and the availability of healthcare services, providing a convenient and secure medium for storing and communicating medical information. However, the adoption of healthcard implementations within the national healthcare systems and IT strategies is relatively slow, blocked by a number of technical, administrative and medical barriers. This paper addresses the above problem by presenting a reference architecture, which facilitates healthcard applications development, covering all healthcare specific requirements and needs. The proposed architecture enables the development of cost-effective, flexible and interoperable healthcard solutions in Java, that make use of ISO 7816-compliant smart cards on different target platforms such as Windows, network computers and handheld devices.

Randomly generated realistic vessel geometry using spline interpolation and 2D Perlin noise

The importance of using artificial vessel walls for examining the effects of the geometry to the risk of plaque rupture, has grown. This has also become essential, mainly because of the lack of an extensive dataset of real vessel geometries that covers an adequate number of cases (e.g. variable amplitude of stenosis for the same vessel). In this paper, we propose a method for creating realistic artificial vessel geometry. The centerline is constructed using PseudoRandom Noise Generator (PRNG) and 3d spline interpolation and the artificial wall using a variation of the 2d Perlin Noise. The resulted geometry indicates that this method achieves realistic representations of vessel walls with user defined parameters.

SIFEM project: Semantic infostructure interlinking an open source finite element tool and libraries with a model repository for the multi-scale modelling of the inner-ear

The SIFEM project targets the development of an infrastructure in order to semantically link open source tools and libraries with existing data as well as new knowledge towards the multi-scale finite element modelling of the inner-ear. The SIFEM system is designed based on an open architecture schema that consists of a set of tools and subsystems in order to develop robust multi-scale models. The project mainly delivers: (i) tools for finite elements modelling, (ii) cochlea reconstruction tool and (iii) 3D inner ear models visualization tool. The main scientific results contribute to the knowledge of alterations associated to diverse cochlear disorders and could lead, in long-term, to personalized healthcare. The overview of the SIFEM platform and its architecture is presented in this paper.

Biologically inspired near extinct system reconstruction

Recovery software system operations from a state of extensive damage without human intervention is a challenging problem as it may need to be based on a different infrastructure from the one that the system was originally designed for and deployed on (i.e., computational and communication devices) and significant reorganization of system functionalities. In this paper, we introduce a bio-inspired approach for reconstructing nearly extinct complex software systems. Our approach is based on encoding a computational DNA (co-DNA) of a system and computational analogues of biological processes to enable the transmission of co-DNA over computational devices and, through it, the transformation of these devices into system cells that can realise chunks of the system functionality, and spread further its reconstruction process.

ACESO: Analysis of Cervical Cancer: An Evidence-Based Treatments Optimization

Deciding for Cervical Cancer (CxCa) treatment is not a simple task. There are several competing factors that arise from the perspective of survival, treatment, toxicity, quality of patient’s life, as well as the geographic location of the patient, which indicates access to specific healthcare resources. All of these factors play a significant role in the ultimate decision to pursue surgery, chemotherapy, and radiation therapy. Our aim is to develop an integrated platform incorporating a big data analytics (BDA) platform, enabling the collection and analysis of heterogeneous data related to the effectiveness of existing interventions and to the discovery of more effective techniques.