A Platform for Health Record Management of the Conscripts in the Hellenic Navy.

eMass project aims to digitalize the medical examination procedure of recruitment phase of conscripts in the Hellenic Navy. eMass integrates recruits' Electronic Health Record (EHR), while allows a pre-screening test, through portable telemedicine equipment. The data will be exploited to assess the individual's cardiovascular risk through appropriate digital tools and algorithms. The eMass digital platform, will be accessible to health experts involved in the recruitment procedure for further assessment and processing. Recruits' personal data is stored in the database encrypted using Advanced Encryption Standard (AES). eMass solution contributes to beneficial management and medical data analysis, preventing inessential physical or medical examinations minimizing danger of possible errors and reducing time-consuming processes. Moreover, eMass exploits Electronic Health Record data through a machine-learning based cardiovascular risk assessment tool.

Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment.

Tinnitus is a common symptom of a phantom sound perception with a considerable socioeconomic impact. Tinnitus pathophysiology is enigmatic and its significant heterogeneity reflects a wide spectrum of clinical manifestations, severity and annoyance among tinnitus sufferers. Although several interventions have been suggested, currently there is no universally accepted treatment. Moreover, there is no well-established correlation between tinnitus features or patients' characteristics and projection of treatment response. At the clinical level, this practically means that selection of treatment is not based on expected outcomes for the particular patient.The complexity of tinnitus and lack of well-adapted prognostic factors for treatment selection highlight a potential role for a decision support system (DSS). A DSS is an informative system, based on big data that aims to facilitate decision-making based on: specific rules, retrospective data reflecting results, patient profiling and predictive models. Therefore, it can use algorithms evaluating numerous parameters and indicate the weight of their contribution to the final outcome. This means that DSS can provide additional information, exceeding the typical questions of superiority of one treatment versus another, commonly addressed in literature.The development of a DSS for tinnitus treatment selection will make use of an underlying database consisting of medical, epidemiological, audiological, electrophysiological, genetic and tinnitus subtyping data. Algorithms will be developed with the use of machine learning and data mining techniques. Based on the profile features identified as prognostic these algorithms will be able to suggest whether additional examinations are needed for a robust result as well as which treatment or combination of treatments is optimal for every patient in a personalized level.In this manuscript we carefully define the conceptual basis for a tinnitus treatment selection DSS. We describe the big data set and the knowledge base on which the DSS will be based and the algorithms that will be used for prognosis and treatment selection.

Personalized Virtual Reality Human-Computer Interaction for Psychiatric and Neurological Illnesses: A Dynamically Adaptive Virtual Reality Environment That Changes According to Real-Time Feedback From Electrophysiological Signal Responses.

Virtual reality (VR) constitutes an alternative, effective, and increasingly utilized treatment option for people suffering from psychiatric and neurological illnesses. However, the currently available VR simulations provide a predetermined simulative framework that does not take into account the unique personality traits of each individual; this could result in inaccurate, extreme, or unpredictable responses driven by patients who may be overly exposed and in an abrupt manner to the predetermined stimuli, or result in indifferent, almost non-existing, reactions when the stimuli do not affect the patients adequately and thus stronger stimuli are recommended. In this study, we present a VR system that can recognize the individual differences and readjust the VR scenarios during the simulation according to the treatment aims. To investigate and present this dynamically adaptive VR system we employ an Anxiety Disorder condition as a case study, namely arachnophobia. This system consists of distinct anxiety states, aiming to dynamically modify the VR environment in such a way that it can keep the individual within a controlled, and appropriate for the therapy needs, anxiety state, which will be called "desired states" for the study. This happens by adjusting the VR stimulus, in real-time, according to the electrophysiological responses of each individual. These electrophysiological responses are collected by an external electrodermal activity biosensor that serves as a tracker of physiological changes. Thirty-six diagnosed arachnophobic individuals participated in a one-session trial. Participants were divided into two groups, the Experimental Group which was exposed to the proposed real-time adaptive virtual simulation, and the Control Group which was exposed to a pre-recorded static virtual simulation as proposed in the literature. These results demonstrate the proposed system's ability to continuously construct an updated and adapted virtual environment that keeps the users within the appropriately chosen state (higher or lower intensity) for approximately twice the time compared to the pre-recorded static virtual simulation. Thus, such a system can increase the efficiency of VR stimulations for the treatment of central nervous system dysfunctions, as it provides numerically more controlled sessions without unexpected variations.

EHealth and Its Role in Supporting Audiological Rehabilitation: Patient Perspectives on Barriers and Facilitators of Using a Personal Hearing Support System With Mobile Application as Part of the EVOTION Study.

BACKGROUND: Hearing loss is a major public health challenge. Audiology services need to utilise a range of rehabilitative services and maximise innovative practice afforded by technology to actively promote personalized, participatory, preventative and predictive care if they are to cope with the social and economic burden placed on the population by the rapidly rising prevalence of hearing loss. Digital interventions and teleaudiology could be a key part of providing high quality, cost-effective, patient-centred management. There is currently very limited evidence that assesses the hearing impaired patient perspective on the acceptance and usability of this type of technology. AIM: This study aims to identify patient perceptions of the use of a hearing support system including a mobile smartphone app when used with Bluetooth-connected hearing aids across the everyday life of users, as part of the EVOTION project. METHODS: We applied a questionnaire to 564 participants in three countries across Europe and analysed the following topics: connectivity, hearing aid controls, instructional videos, audiological tests and auditory training. KEY FINDINGS: Older users were just as satisfied as younger users when operating this type of technology. Technical problems such as Bluetooth connectivity need to be minimised as this issue is highly critical for user satisfaction, engagement and uptake. A system that promotes user-controllability of hearing aids that is more accessible and easier to use is highly valued. Participants are happy to utilise monitoring tests and auditory training on a mobile phone out of the clinic but in order to have value the test battery needs to be relevant and tailored to each user, easy to understand and use. Such functions can elicit a negative as well as positive experience for each user. CONCLUSION: Older and younger adults can utilise an eHealth mobile app to complement their rehabilitation and health care. If the technology works well, is tailored to the individual and in-depth personalised guidance and support is provided, it could assist maximisation of hearing aid uptake, promotion of self-management and improving outcomes.

Comparison between Full Body Motion Recognition Camera Interaction and Hand Controllers Interaction used in Virtual Reality Exposure Therapy for Acrophobia.

Virtual Reality has already been proven as a useful supplementary treatment tool for anxiety disorders. However, no specific technological importance has been given so far on how to apply Virtual Reality with a way that properly stimulates the phobic stimulus and provide the necessary means for lifelike experience. Thanks to technological advancements, there is now a variety of hardware that can help enhance stronger emotions generated by Virtual Reality systems. This study aims to evaluate the feeling of presence during different hardware setups of Virtual Reality Exposure Therapy, and, particularly how the user's interaction with those setups can affects their sense of presence during the virtual simulation. An acrophobic virtual scenario is used as a case study by 20 phobic individuals and the Witmer-Singer presence questionnaire was used for presence evaluation by the users of the system. Statistical analysis on their answers revealed that the proposed full body Motion Recognition Cameras system generates a better feeling of presence compared to the Hand Controllers system. This is thanks to the Motion Recognition Cameras, which track and allow display of the user's entire body within the virtual environment. Thus, the users are enabled to interact and confront the anxiety-provoking stimulus as in real world. Further studies are recommended, in which the proposed system could be used in Virtual Reality Exposure Therapy trials with acrophobic patients and other anxiety disorders as well, since the proposed system can provide natural interaction in various simulated environments.

Utilizing Key Item Method to Manage Musculoskeletal Disorders in a Hospital Workplace.

Management of musculoskeletal disorders (MSDs) is a necessity for the modern work environment. In hospitals, these disorders have a particularly high frequency among health care workers whose work entails lifting and transporting patients as well as washing, dressing and feeding them. This paper, presents an electronic application which is based on the method of basic items (KIM - Key Item Method) in order to reduce incidents of MSDs resulting from manual transport of loads in healthcare facilities. The sample consisted of 15 female hospital meal servers from Metaxa Hospital (Piraeus, Greece) in order to assess the activities of lifting, carrying, transporting, pushing and pulling of loads which are part of their daily work duties. The key requirement for the application was not only helping the risk assessment but also leading to targeted, easily applicable and low cost corrective measures. The results of this electronic tool application showed increased usability and benefits which were associated with the used database and the detailed information relatively to the corrective measures, such as training of the employees to change body posture, replacement of wheels on trolleys and redesigning of serving aisles which proved beneficial.

Emotional stimulation during motor exercise: An integration to the holistic rehabilitation framework.

Over the recent years, plenty of studies have been carried out concerning disabilities and rehabilitation. However, very few of them have focused on proposing or creating a holistic framework meant to maximize the merits of rehabilitation treatments. More importantly, an even smaller number has taken into consideration the immense impact of emotions and the crucial role they display concerning patients' performance during rehabilitation. The purpose of this study is to examine and highlight the direct connection of patients' performance quality with an artificially induced positive or negative emotional state. For that reason, we conducted an experiment regarding the convergence of emotions with the motor exercise. Specifically, our study was conducted with 45 participants and the GAPED database was employed as an emotional classifier. The effect of the visual stimulus was combined with a simple bicep exercise which introduces the motor element in our study. Statistical analysis of the yielding EMG & Acellerometer signals demonstrated a considerable difference in the quality of the physical activity of each subject induced by different emotional triggers. As a result, it is pivotal to integrate a holistic approach to rehabilitation in future clinical trials, aiming to reinforce the patient involvement and engagement process and thus the efficacy of the treatment.

Evaluation for the Necessity of Medical Imaging Tests Prescription in Neurology.

The evaluation and control algorithms for the necessity of medical prescription testing, comprises useful tool for health professionals. It is beyond doubt that a connection between illness, symptoms, medical tests and prescriptions is essential and thus algorithms facilitating such approaches should be available to health professionals. Such informatics tools require the implementation of smart, interactive tools and not just linear, information storing websites. Such algorithms should be dynamic, that is their output should change based on the input as for example, in the serial input of symptoms to clinical examination to subsequent diagnosis. Slight variations in symptomatology can greatly alter diagnosis and subsequent physical testing and prescription. The present work presents a novel algorithm for the control of medical prescription testing in neurology, by utilizing decision trees for the connection of symptomatology to diagnosis and prescription for neurological conditions and disease. To the best of our knowledge this is the first time that such an approach is proposed.

The EVOTION Decision Support System: Utilizing It for Public Health Policy-Making in Hearing Loss.

As Decision Support Systems start to play a significant role in decision making, especially in the field of public-health policy making, we present an initial attempt to formulate such a system in the concept of public health policy making for hearing loss related problems. Justification for the system's conceptual architecture and its key functionalities are presented. The introduction of the EVOTION DSS sets a key innovation and a basis for paradigm shift in policymaking, by incorporating relevant models, big data analytics and generic demographic data. Expected outcomes for this joint effort are discussed from a public-health point of view.

An Internet of Things platform architecture for supporting ambient assisted living environments.

Internet of Things (IoT) is the logical further development of today's Internet, enabling a huge amount of devices to communicate, compute, sense and act. IoT sensors placed in Ambient Assisted Living (AAL) environments, enable the context awareness and allow the support of the elderly in their daily routines, ultimately allowing an independent and safe lifestyle. The vast amount of data that are generated and exchanged between the IoT nodes require innovative context modeling approaches that go beyond currently used models. Current paper presents and evaluates an open interoperable platform architecture in order to utilize the technical characteristics of IoT and handle the large amount of generated data, as a solution to the technical requirements of AAL applications.

PAT: an intelligent authoring tool for facilitating clinical trial design.

Great investments are made by both private and public funds and a wealth of research findings is published, the research and development pipeline phases quite low productivity and tremendous delays. In this paper, we present a novel authoring tool which has been designed and developed for facilitating study design. Its underlying models are based on a thorough analysis of existing clinical trial protocols (CTPs) and eligibility criteria (EC) published in clinicaltrials.gov by domain experts. Moreover, its integration with intelligent decision support services and mechanisms linking the study design process with healthcare patient data as well as its direct access to literature designate it as a powerful tool offering great support to researchers during clinical trial design.

Novel mobile-health technologies.

Mobile Health is fast becoming one of the fastest growing sectors of health as health tries to shift to patient-centric solutions. Putting the patients in the centre of care also forces them to take control of their wellbeing, health management and/or disease management. This is especially relevant when the management is constant as it is the case with chronic diseases.

Smart adaptable system for older adults' Daily Life Activities Management - The ABLE platform.

In this paper we propose a system (ABLE) that will act as the main platform for a number of low-cost, mature technologies that will be integrated in order to create a dynamically adaptive Daily Life Activities Management environment in order to facilitate the everyday life of senior (but not exclusively) citizens at home. While the main target group of ABLE's users is the ageing population its use can be extended to all people that are vulnerable or atypical in body, intellect or emotions and are categorized by society as disabled. The classes of assistive products that are well defined in the international standard, ISO9999 such as assistive products for personal medical treatment, personal care and protection, communication, information and reaction and for personal mobility, will be easily incorporated in our proposed platform. Furthermore, our platform could integrate and implement the above classes under several service models that will be analyzed further.

A comparative study of pattern recognition classifiers to predict physical activities using smartphones and wearable body sensors.

This paper presents a wireless body area network platform that performs physical activities recognition using accelerometers, biosignals and smartphones. Multiple classifiers and sensor combinations were examined to identify the classifier with the best recognition performance for the static and dynamic activities. The Functional Trees classifier proved to provide the best results among the classifiers evaluated (Naive Bayes, Bayesian Networks, Support Vector Machines and Decision Trees [C4.5, Random Forest]) and was used to train the model which was implemented for the real time activity recognition on the smartphone. The identified patterns of daily physical activities were used to examine conformance with medical advice, regarding physical activity guidelines. An algorithm based on Skip Chain Conditional Random Fields, received as inputs the recognized activities and data retrieved from the GPS receiver of the smartphone to develop dynamic daily patterns that enhance prediction results. The presented platform can be extended to be used in the prevention of short-term complications of metabolic diseases such as diabetes.

Elucidating the identity of resistance mechanisms to prednisolone exposure in acute lymphoblastic leukemia cells through transcriptomic analysis: A computational approach.

BACKGROUND: It has been shown previously that glucocorticoids exert a dual mechanism of action, entailing cytotoxic, mitogenic as well as cell proliferative and anti-apoptotic responses, in a dose-dependent manner on CCRF-CEM cells at 72 h. Early gene expression response implies a dose-dependent dual mechanism of action of prednisolone too, something reflected on cell state upon 72 h of treatment. METHODS: In this work, a generic, computational microarray data analysis framework is proposed, in order to examine the hypothesis, whether CCRF-CEM cells exhibit an intrinsic or acquired mechanism of resistance and investigate the molecular imprint of this, upon prednisolone treatment. The experimental design enables the examination of both the dose (0 nM, 10 nM, 22 uM, 700 uM) effect of glucocorticoid exposure and the dynamics (early and late, namely 4 h, 72 h) of the molecular response of the cells at the transcriptomic layer. RESULTS: In this work, we demonstrated that CCRF-CEM cells may attain a mixed mechanism of response to glucocorticoids, however, with a clear preference towards an intrinsic mechanism of resistance. Specifically, at 4 h, prednisolone appeared to down-regulate apoptotic genes. Also, low and high prednisolone concentrations up-regulates genes related to metabolism and signal-transduction in both time points, thus favoring cell proliferative actions. In addition, regulation of NF-κB-related genes implies an inherent mechanism of resistance through the established link of NF-κB inflammatory role and GC-induced resistance. The analysis framework applied here highlights prednisolone-activated regulatory mechanisms through identification of early responding sets of genes. On the other hand, study of the prolonged exposure to glucocorticoids (72 h exposure) highlights the effect of homeostatic feedback mechanisms of the treated cells. CONCLUSIONS: Overall, it appears that CCRF-CEM cells in this study exhibit a diversified, combined pattern of intrinsic and acquired resistance to prednisolone, with a tendency towards inherent resistant characteristics, through activation of different molecular courses of action.

E-Health towards ecumenical framework for personalized medicine via Decision Support System.

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.

Mobile phone technologies and advanced data analysis towards the enhancement of diabetes self-management.

Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patient's self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.

2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection.

The paper addresses the fine retinal-vessel's detection issue that is faced in diagnostic applications and aims at assisting in better recognizing fine vessel anomalies in 2D. Our innovation relies in separating key visual features vessels exhibit in order to make the diagnosis of eventual retinopathologies easier to detect. This allows focusing on vessel segments which present fine changes detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the same vessel detection procedure. We first carry out an initial estimate of the basic vessel-wall's network, define the main wall-body, and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs) yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency). Specific vessel parameters (centerline, width, location, fall-away rate, main orientation) are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs). These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off. Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection.

Using digital watermarking to enhance security in wireless medical image transmission.

OBJECTIVES: During the last few years, wireless networks have been increasingly used both inside hospitals and in patients' homes to transmit medical information. In general, wireless networks suffer from decreased security. However, digital watermarking can be used to secure medical information. In this study, we focused on combining wireless transmission and digital watermarking technologies to better secure the transmission of medical images within and outside the hospital. METHODS: We utilized an integrated system comprising the wireless network and the digital watermarking module to conduct a series of tests. RESULTS: The test results were evaluated by medical consultants. They concluded that the images suffered no visible quality degradation and maintained their diagnostic integrity. DISCUSSION: The proposed integrated system presented reasonable stability, and its performance was comparable to that of a fixed network. This system can enhance security during the transmission of medical images through a wireless channel.

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer.

BACKGROUND: In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. METHODS: We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 x 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. RESULTS: For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium. CONCLUSION: This study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal).