A Prototype of the National EHR system for Cyprus.

The aim of this paper is to present Cyprus' initiative for the design and the implementation of the prototype of the integrated electronic health record at a national level that will establish the foundations of the country's broader eHealth ecosystem. The latter, requires an interdisciplinary approach and scientific collaboration among various fields, including medicine, information and communication technologies, management, and finance, among others. The objective, is to design the system architecture, specify the requirements in terms of clinical content as well as the hardware infrastructure, but also implement European and national legislation with respect to privacy and security that govern sensitive medical data manipulation. The present study summarizes the outcomes of the 1st phase of this initiative, which comprises of the healthcare as well as the administrative requirements, user stories, data-flows and associated functionality. Moreover, leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) standard we highlight the concluded interoperability framework that allows genuine cross-system communication and defines third-party systems connectivity.Clinical Relevance- This work is strongly correlated with medicine since it describes the system requirements and the architecture of a national integrated electronic health records system.

Integration of Biobanks in National eHealth Ecosystems Facilitating Long-Term Longitudinal Clinical-Omics Studies and Citizens' Engagement in Research Through eHealthBioR.

Biobanks have long existed to support research activities with BBMRI-ERIC formed as a European research infrastructure supporting the coordination for biobanking with 20 country members and one international organization. Although the benefits of biobanks to the research community are well-established, the direct benefit to citizens is limited to the generic benefit of promoting future research. Furthermore, the advent of General Data Protection Regulation (GDPR) legislation raised a series of challenges for scientific research especially related to biobanking associate activities and longitudinal research studies. Electronic health record (EHR) registries have long existed in healthcare providers. In some countries, even at the national level, these record the state of the health of citizens through time for the purposes of healthcare and data portability between different providers. The potential of EHRs in research is great and has been demonstrated in many projects that have transformed EHR data into retrospective medical history information on participating subjects directly from their physician's collected records; many key challenges, however, remain. In this paper, we present a citizen-centric framework called eHealthBioR, which would enable biobanks to link to EHR systems, thus enabling not just retrospective but also lifelong prospective longitudinal studies of participating citizens. It will also ensure strict adherence to legal and ethical requirements, enabling greater control that encourages participation. Citizens would benefit from the real and direct control of their data and samples, utilizing technology, to empower them to make informed decisions about providing consent and practicing their rights related to the use of their data, as well as by having access to knowledge and data generated from samples they provided to biobanks. This is expected to motivate patient engagement in future research and even leads to participatory design methodologies with citizen/patient-centric designed studies. The development of platforms based on the eHealthBioR framework would need to overcome significant challenges. However, it would shift the burden of addressing these to experts in the field while providing solutions enabling in the long term the lower monetary and time cost of longitudinal studies coupled with the option of lifelong monitoring through EHRs.

Conditional Generative Denoising Autoencoder.

We present a generative denoising autoencoder model that has an embedded data classifier in its architecture in order to take advantage of class-based discriminating features and produce better data samples. The proposed model is a conditional generative model and is sampled with a Markov chain Monte Carlo (MCMC) process according to a label that denotes the desired (or undesired) class or classes. In this sense, any chosen predefined class or characteristic may have a positive or negative effect on the image generation process, meaning that it can be instructed to be present or absent from the generated sample. We argue that allowing discriminative information in the form of feature detectors to be present in the latent representation of the autoencoder can be generally beneficial. This technique is an alternative approach to variational autoencoders (VAEs) that enforce a prior on the latent distribution. We further claim that supervised learning may be generally able to serve unsupervised learning through an interaction between the two paradigms. However, the extreme majority of research done on the interaction of the two learning regimes has the goal of using unsupervised learning to improve supervised learning. In this article, we explore the two learning paradigms' interaction in the opposite direction.

Proposal for an eHealth Based Ecosystem Serving National Healthcare.

The European Union (EU)'s keen concern about citizens' health and well-being advancement has been expressed at all levels. It has been understood that at present, these can only be achieved through coordinated actions at the individual member states' level based on EU directives, as well as through promoting and funding R&D and expanding the use of eHealth technologies. Despite the diversities and particularities among member states, common values such as universal access to good quality healthcare, equity, and solidarity have been widely accepted across EU. That demanded the adoption of policies and follow directives, which streamlined actions to bridge healthcare gaps, and facilitate cross-border healthcare. This paper articulates a framework for deriving a national healthcare system, based on interoperable Electronic Health Record (EHR) with safeguarding healthcare quality, enabling quadruple helix (Public, Academia, Industry, NGOs) driven R&D and guided by a patient-centered approach. A methodology to develop an integrated EHR at National level is proposed as a prerequisite for eHealth and put into perspective. Recommendations are given for the steps needed, from the managerial, legal, technical, and financial concerns in developing an open access, patient-centered national healthcare system based on the context and constraints of a country. The example of a small country to apply the proposed methodology is demonstrated. Stakeholders, including citizens, healthcare professionals, academia, and the industry are mobilized, enabled, and incentivized for implementing the methodology. Experiences are aspired to be offered as lessons learned for other countries to adapt on their environment.

Intelligent Noninvasive Diagnosis of Aneuploidy: Raw Values and Highly Imbalanced Dataset.

The objective of this paper is to introduce a noninvasive diagnosis procedure for aneuploidy and to minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method by using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets1 with a total of 122 362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus. This study, unlike previous studies published by the authors for a similar problem differs in three basic principles: 1) the training of the artificial neural networks is done by using the markers' values in their raw form (unprocessed), 2) a balanced training dataset is created and used by selecting only a representative number of euploids for the training phase, and 3) emphasis is given to the financials and suggest hierarchy and necessity of the available tests. The proposed artificial neural networks models were optimized in the sense of reaching a minimum false positive rate and at the same time securing a 100% detection rate for Trisomy 21. These systems correctly identify other aneuploidies (Trisomies 13&18, Turner, and Triploid syndromes) at a detection rate greater than 80%. In conclusion, we demonstrate that artificial neural network systems can contribute in providing noninvasive, effective early screening for fetal aneuploidies with results that compare favorably to other existing methods.

First Trimester Noninvasive Prenatal Diagnosis: A Computational Intelligence Approach.

The objective of this study is to examine the potential value of using machine learning techniques such as artificial neural network (ANN) schemes for the noninvasive estimation, at 11-13 weeks of gestation, the risk for euploidy, trisomy 21 (T21), and other chromosomal aneuploidies (O.C.A.), from suitable sonographic, biochemical markers, and other relevant data. A database(1) (1)The dataset can become available for academic purposes by communicating directly with the authors.

Medical Content Searching, Retrieving, and Sharing Over the Internet: Lessons Learned From the mEducator Through a Scenario-Based Evaluation.

BACKGROUND: The mEducator Best Practice Network (BPN) implemented and extended standards and reference models in e-learning to develop innovative frameworks as well as solutions that enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, and re-purposed across European Institutions, targeting medical students, doctors, educators and health care professionals. Scenario-based evaluation for usability testing, complemented with data from online questionnaires and field notes of users' performance, was designed and utilized for the evaluation of these solutions. OBJECTIVE: The objective of this work is twofold: (1) to describe one instantiation of the mEducator BPN solutions (mEducator3.0 - "MEdical Education LINnked Arena" MELINA+) with a focus on the metadata schema used, as well as on other aspects of the system that pertain to usability and acceptance, and (2) to present evaluation results on the suitability of the proposed metadata schema for searching, retrieving, and sharing of medical content and with respect to the overall usability and acceptance of the system from the target users. METHODS: A comprehensive evaluation methodology framework was developed and applied to four case studies, which were conducted in four different countries (ie, Greece, Cyprus, Bulgaria and Romania), with a total of 126 participants. In these case studies, scenarios referring to creating, sharing, and retrieving medical educational content using mEducator3.0 were used. The data were collected through two online questionnaires, consisting of 36 closed-ended questions and two open-ended questions that referred to mEducator 3.0 and through the use of field notes during scenario-based evaluations. RESULTS: The main findings of the study showed that even though the informational needs of the mEducator target groups were addressed to a satisfactory extent and the metadata schema supported content creation, sharing, and retrieval from an end-user perspective, users faced difficulties in achieving a shared understanding of the meaning of some metadata fields and in correctly managing the intellectual property rights of repurposed content. CONCLUSIONS: The results of this evaluation impact researchers, medical professionals, and designers interested in using similar systems for educational content sharing in medical and other domains. Recommendations on how to improve the search, retrieval, identification, and obtaining of medical resources are provided, by addressing issues of content description metadata, content description procedures, and intellectual property rights for re-purposed content.

A patient centered electronic health: eHealth system development.

BACKGROUND: Medical practice and patient-doctor relationship will continue improving while technology is integrated in our everyday life. In recent years the term eHealth landmarked a new era with improved health provider's skills and knowledge, and increased patient participation in medical care activities. OBJECTIVE: To show why the design and implementation of a healthcare system needs to follow a specific philosophy dictated by the level of eHealth maturity of a country and its citizens. METHODS: Based on the maturity level, an adaptable framework for implementing an Electronic Health System at national level is derived, guided by the Patient Centered Philosophy as defined and introduced by the EU directives. Implementation prerequisites are analyzed together with guiding principles for identifying the maturity level of an organization or country. RESULTS: Cyprus being a small EU country, it can be used as pilot site for the whole Europe, was chosen for this study and its maturity level analysis is presented. Recommendations that determine general steps needed to prepare the ground for an adequate patient-centered national healthcare system are accompanied. CONCLUSION: The implementation of an integrated Electronic Health Record at National level, as a prerequisite for a patient-centered eHealth environment is evidently demonstrated.

Exploitation of Medical Crisp Database for Fuzzy Diagnostic Decision Support Systems.

There are several types of Diagnostic Decision Support Systems (DDSS) but all move towards a common direction: provide assistance to the doctors/clinicians to make the right diagnosis for a specific patient, minimizing as much as possible the needed time for this. In doing so, some DDSS systems exploit existing crisp medical databases while others take an advantage of human knowledge and experience, by building fuzzy medical databases from scratch. It would be of interest though, as well as time saving, to combine these two together and examine how one can actually use an existing crisp medical dataset to transform it into a new fuzzy medical database with parameters being expressed and defined based on the clinicians' way of thinking. A methodology implementing this task is proposed in this paper accompanied by the results of its application on a real crisp medical database.

eHealth Services for the European Reference Network on Rare Anaemias (eENERCA).

This paper presents an electronic registry system for the purposes of the eENERCA for rare congenital conditions that require lifelong follow up and treatment. The main objective of the eENERCA project focusses on the prevention of major rare anaemias (RAs) by facilitating the access, at a European level, to the best genetic counselling, diagnosis and clinical management of the patients with RA independently of their country of origin. This can be achieved by promoting an extension of the full Electronic Health Record system and specifically the electronic registries for RAs, across Europe for the purposes stated hence promoting service development for the benefit of patients. The proposed eRegistry will serve as an epidemiological tool to improve the management of patient services and ultimately improve patient care.

Integrated Electronic Health Record Database Management System: A Proposal.

eHealth has attained significant importance as a new mechanism for health management and medical practice. However, the technological growth of eHealth is still limited by technical expertise needed to develop appropriate products. Researchers are constantly in a process of developing and testing new software for building and handling Clinical Medical Records, being renamed to Electronic Health Record (EHR) systems; EHRs take full advantage of the technological developments and at the same time provide increased diagnostic and treatment capabilities to doctors. A step to be considered for facilitating this aim is to involve more actively the doctor in building the fundamental steps for creating the EHR system and database. A global clinical patient record database management system can be electronically created by simulating real life medical practice health record taking and utilizing, analyzing the recorded parameters. This proposed approach demonstrates the effective implementation of a universal classic medical record in electronic form, a procedure by which, clinicians are led to utilize algorithms and intelligent systems for their differential diagnosis, final diagnosis and treatment strategies.

Personalized medicine - a challenge for cyprus.

We were told at school, "If you want to solve a problem you have to identify it first", you are then halfway towards the solution. This implied of course that the other half of the solution is a straight forward exercise, or it can be provided by a standard predetermined procedure which can be found on a shelf of ready-made solutions. This procedure, even though it has limitations, was followed extensively in solving medical problems and proved to be successful especially in cases where the available options for the solution were limited, and the only criteria to determine the final solution were cost, convenience, time, availability, etc. In other words the solution-treatment was primarily based on predetermined options, and not patient based.

A patient centered electronic health system: an example for cyprus.

The design and implementation of a healthcare system needs to follow a specific philosophy regarding its operational structure and must be adapted gradually with one step at a time, depending on the level of maturity of a country on certain key issues. The main goal of this paper is to present an overall recommendations framework for implementing an Electronic Health System at national level, guided by the Patient Centered Philosophy. Certain prerequisites for implementing such systems are analyzed together with guiding principles for identifying the maturity level of an organization or country. The maturity level analysis for Cyprus is presented and is accompanied by some recommendations that determine the steps needed to prepare the ground for a complete patient centered national healthcare system.

Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.