Haridimos Kondylakis

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The explosion of the web and the abundance of linked data demand effective and efficient methods for storage, management, and querying. Apache Spark is one of the most widely used engines for big data processing, with more and more systems adopting it for efficient query answering. Existing approaches exploiting Spark for querying RDF data, adopt partitioning techniques for reducing the data that need to be accessed in order to improve efficiency. However, simplistic data partitioning fails, on one hand, to minimize data access and on the other hand to group data usually queried together. This is translated into limited improvement in terms of efficiency in query answering. In this paper, we present DIAERESIS, a novel platform that accepts as input an RDF dataset and effectively partitions it, minimizing data access and improving query answering efficiency. To achieve this, DIAERESIS first identifies the top-k most important schema nodes, i.e., the most important classes, as centroids and distributes the other schema nodes to the centroid they mostly depend on. Then, it allocates the corresponding instance nodes to the schema nodes they are instantiated under. Our algorithm enables fine-tuning of data distribution, significantly reducing data access for query answering. We experimentally evaluate our approach using both synthetic and real workloads, strictly dominating existing state-of-the-art, showing that we improve query answering in several cases by orders of magnitude.

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Artificial Intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population and diseases diversity. However, to date, efforts are fragmented, based on single–institution, size- and annotation-limited datasets. Available public datasets (e.g., US TCIA) are limited in scope, making model generalizability really difficult. In this direction, five EU projects are currently working on the development of big data infrastructures that will enable European, ethically and GDPR-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will co-exist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.

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Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I-III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n=328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n=50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve=0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.

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The explosion of big data and artificial intelligence has rapidly increased the need for integrated, homogenized, and harmonized health data. Many common data models (CDM) and standard vocabularies have appeared in an attempt to offer harmonized access to the available information, with Observational Medical Outcomes Partnership (OMOP) CDM being one of the most prominent ones, allowing the standardization and harmonization of healthcare information. However, despite its flexibility, still capturing imaging metadata along with the corresponding clinical data continues to pose a challenge due to the lack of a comprehensive standard representation for image-related information, and subsequent curation processes such as image segmentation, image motion correction, co-registration, etc. In this paper, we present RT-CDM, an extension of the OMOP-CDM for tomography imaging data. We present our modeling choices and we demonstrate how data for more than 9,600 patients, has been already acquired and integrated using the presented RT-CDM extension.

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The COVID-19 pandemic has catapulted healthcare research as a top priority for many nations. Researchers have used data-driven approaches to better understand COVID-19, develop effective vaccines, and mitigate the spread of the virus. Healthcare data management continues to evolve as mankind faces the biggest public health crisis of modern times. As new kinds of data emerge, new models, algorithms, and techniques are needed to better harness the value of healthcare data for advanced decision making. On the other hand, Semantic Web technologies can provide effective solutions for enabling interoperability and common language among healthcare systems, and can lead to the disambiguation of the information through the adoption of various terminologies and ontologies available. In addition, AI and machine learning can enable data-driven decision making and extracting meaningful insights from complex healthcare datasets. Thus, knowledge representation and reasoning on healthcare data become even more important. Semantic Web technologies have matured over the years and can provide these capabilities by design. The purpose of this Special Issue is to collect contributions on the cross-cutting the fields of Semantic Web, data science, data management, and health informatics to discuss the challenges in healthcare data management and to propose novel and practical solutions for the next generation of data-driven healthcare systems. The ultimate goal is to enable new innovations in Semantic Web, knowledge management, and data management for healthcare systems to move the needle to achieve the vision of precision medicine.

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Proper and well-timed interventions may improve breast cancer patient adaptation and quality of life (QoL) through treatment and recovery. The challenge is to identify those patients who would benefit most from a particular intervention. The aim of this study was to measure whether the machine learning prediction incorporated in the clinical decision support system (CDSS) improves clinicians’ performance to predict patients’ QoL during treatment process. We conducted two user experiments in which clinicians used a CDSS to predict QoL of breast cancer patients. In both experiments each patient was evaluated both with and without the aid of a machine learning (ML) prediction. In Experiment I, 60 breast cancer patients were evaluated by 6 clinicians. In Experiment II, 90 patients were evaluated by 9 clinicians. The task of clinicians was to predict the patient’s quality of life at either 6 (Experiment I) or 12 months post-diagnosis (Experiment II). Taking into account input from the CDSS considerably improved clinicians’ prediction accuracy. Accuracy of clinicians for predicting QoL of patients at 6 months post-diagnosis was .745 (95% CI .668-.821) with the aid of the prediction provided by the ML model and .696 (95% CI .608-.781) without the aid. Clinicians’ prediction accuracy at 12 months was .739 (95% CI .667-.812) with the aid and .709 (95% CI .633- .783) without the aid. When the machine learning model’s prediction was correct, the average accuracy of the clinicians for predicting QoL at 6 months was .793 (95% CI .739-.838) with the aid and .720 (95% CI .636-.798) without the aid. Corresponding prediction accuracy of QoL at 12 months was .909 (95% CI .881-.936) and .827 (95% CI .782-.871).

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Knowledge graphs are becoming more and more prevalent on the web, ranging from small taxonomies, to large knowledge bases containing a vast amount of information. To construct such knowledge graphs either automatically or manually, tools are necessary for their quick exploration and understanding. Semantic summaries have been proposed as a key technology enabling the quick understanding and exploration of large knowledge graphs. Among the methods proposed for generating summaries, structural methods exploit primarily the structure of the graph in order to generate the result summaries. Approaches in the area focus on identifying the most important nodes and usually employ a single centrality measure, capturing a specific perspective on the notion of a node’s importance. Moving from one centrality measure to many however, has the potential to generate a more objective view on nodes’ importance, leading to better summaries. In this paper, we present SumMER, the first structural summarization technique exploiting machine learning techniques for RDF/S KGs. SumMER explores eight centrality measures and then exploits machine learning techniques for optimally selecting the most important nodes. Then those nodes are linked formulating a subgraph out of the original graph. We experimentally show that combining centrality measures with machine learning effectively increases the quality of the generated summaries.

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Background: Major chronic diseases such as cardiovascular disease, diabetes, and cancer impose a significant burden on people and the healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential towards the development of intelligent mobile health (mHealth) interventions for chronic diseases which could revolutionize the delivery of healthcare anytime-anywhere. Objective: To present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases, and advance our understanding of the progress made in this rapidly developing field. Methods: Search was conducted on the bibliographic databases of Scopus and PubMed in order to identify papers with a focus on the deployment of DL algorithms, using data captured from mobile devices (e.g., smartphones, smartwatches, and other wearable devices), targeting cardiovascular disease, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, the study period, as well as the employed DL algorithm, the main DL outcome, the dataset used, the features selected, and the achieved performance. Results: 20 studies were included in the review. 7 DL studies (35%) targeted cardiovascular disease, 9 studies (45%) targeted diabetes, and 4 studies (20%) targeted cancer. The most common DL outcome was the diagnosis of the patient condition for the cardiovascular disease studies, prediction of blood glucose values for studies in diabetes, and early detection of cancer. Most of the DL algorithms employed were convolutional neural networks in studies for cardiovascular disease and cancer, and recurrent neural networks in studies for diabetes. The performance of DL was found overall to be satisfactory reaching more than 84% accuracy in the majority of the studies. In comparison with classic machine learning approaches, DL was found to achieve better performance almost in all studies which reported such comparison outcomes. The vast majority of the studies did not provide details on the explainability of DL outcomes. Conclusions: The use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases through harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions.

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Given the huge amount of imaging data that are becoming available worldwide, and the incredible range of possible improvements that artificial intelligence algorithms can provide in clinical care for diagnosis and decision support, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. In medical imaging, metadata are additional data associated with the images, that provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the HORIZON 2020 “AI for Health Imaging” projects, which are all dedicated to the creation of imaging biobanks.

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Informal care is considered to be important for the wellbeing and resilience of the elderly. However, solutions for the effective collaboration of healthcare professionals, patients, and informal caregivers are not yet widely available. The purpose of this paper is to present the development of a digital platform that uses innovative tools and artificial intelligence technologies to support care coordination and shared care planning for elder care, with a particular focus on frailty. The challenges of shared care planning in the coordination of frailty care are demonstrated, followed by presentation of the design and technical architecture of an integrated platform. The platform incorporates all elements essential for the support of daily activities, coordinated care, and timely interventions in case of emergency and need. This paper describes the challenges involved in implementing the platform and concludes by reporting the necessary steps required in order to establish effective smart care for the elderly.

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Background: Stress and anxiety are psychophysiological responses commonly experienced by patients during a perioperative process that can increase pre- and post-surgical complications to a comprehensive and positive recovery. Preventing and intervening stress and anxiety can help patients achieve positive outcomes on health and wellbeing. Similarly, provision of education about the surgery can be a crucial component and it is inversely correlated to preoperative anxiety levels. However, few patients receive stress and anxiety relief support prior to a surgery and, resource constraints make face-to-face education sessions untenable. Digital health (DH) interventions can be helpful in empowering patients and enhancing a more positive experience. DH interventions are showing to help patients feel informed about possible benefits and risks of available treatment options. However, currently they focus only on providing informative content, neglecting the importance of personalization and patient empowerment. Objective: This study aims to explore the feasibility of a DH intervention called CARINAE designed to provide personalized stress- and anxiety-management evidence-based methods enabled by a comprehensive digital ecosystem that incorporates wearable, mobile and virtual reality technologies. CARINAE includes the use of advanced data-driven techniques for tailored patient education and lifestyle support. Methods: The trial will include 5 hospitals across 3 European countries, and will use a randomized-controlled design including 30 intervention participants and 30 control group participants. The involved surgeries are cardiopulmonary and coronary artery bypass surgeries, cardiac valve replacement, prostate or bladder cancer surgeries, hip and knee replacement, maxillofacial surgery, or scoliosis. The control group will receive the standard care and the intervention group will additionally be exposed to the CARINAE intervention. Results: The recruitment process starts by January 2022, and the primary impact analysis is expected to be conducted by May 2022. Conclusions: This manuscript details a comprehensive protocol for a study that will provide valuable information about the CARINAE intervention such as the measurement of comparative intervention effects on stress, anxiety and pain management and usability by patients, caregivers and healthcare professionals. This will contribute to the evidence planning process for the future adoption of diverse DH solutions in the surgery field. Clinical Trial: Trial was registered in ClinicalTrials.gov: NCT05184725.

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More and more weakly structured, and irregular data sources are becoming available every day. The schema of these sources is useful for a number of tasks, such as query answering, exploration and summarization. However, although semantic web data might contain schema information, in many cases this is completely missing or partially defined. In this paper, we present a survey of the state of the art on schema information extraction approaches. We analyze and classify these approaches into three families: (i) approaches that exploit the implicit structure of the data, without assuming that some explicit statements on the schema are provided in the dataset; (ii) approaches that use the explicit schema statements contained in the dataset to complement and enrich the schema, and (iii) those that discover structural patterns contained in a dataset. We compare these studies in terms of their approach, advantages and limitations. Finally we discuss the problems that remain open.

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Creating a holistic view on patient data comes withmany challenges but also brings many benefits for dis-ease prediction, prevention, diagnosis and treatment.Especially in the COVID-19 era, this is more importantthan ever before. The third International Workshop onSemantic Web Meets Health Data Management (SWH)was aimed at bringing together an interdisciplinary audi-ence who was interested in the fields of Semantic Web,data management and health informatics. The workshopgoal was to discuss the challenges in healthcare datamanagement and to propose new solutions for the nextgeneration data-driven healthcare systems. In this arti-cle, we summarize the outcomes of the workshop, andwe present a number of key observations and researchdirections that emerged from presentations.

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This article studies opinion mining from social media with probabilistic logic reasoning. As it is known, Twitter is one of the most active social networks, with millions of tweets sent daily, where multiple users express their opinion about travelling, economic issues, political decisions etc. As such, it offers a valuable source of information for opinion mining. In this paper we present OpinionMine, a Bayesian-based framework for opinion mining, exploiting Twitter Data. Initially, our framework imports Tweets massively by using Twitter’s API. Next, the imported Tweets are further processed automatically for constructing a set of untrained rules and random variables. Then, a Bayesian Network is derived by using the set of untrained rules, the random variables and an evidence set. After that, the trained model can be used for the evaluation of new Tweets. Finally, the constructed model can be retrained incrementally, thus becoming more robust. As application domain for the development of our methodology we have selected tourism because it is one of the most popular topics in social media. Our framework can predict users’ intention to visit a place. Among the advantages of our framework is that it follows an incremental learning strategy. That is, the derived model can be retrained incrementally with new training sets thus becoming more robust. Further, our framework can be easily adapted to opinion mining from social media on other topics, whereas the rules of the derived model are constructed in an efficient way and automatically.

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Ontologies are widely used nowadays. However, the plethora of ontologies currently available online, makes it really difficult to identify which ontologies are appropriate for a given task and to decide on their quality characteristics. This is further complicated, by the fact that multiple quality criteria have been proposed for ontologies making it even more difficult to decide which ontology to adopt. In this direction, in this paper, we present Delta, a modular online tool for analyzing and evaluating ontologies. The interested user can upload an ontology to the tool, which, then automatically analyzes it and graphically visualizes numerous statistics, metrics, and pitfalls. Those visuals presented include a diverse set of quality dimensions, further guiding users to understand the benefits and the drawbacks of each individual ontology and how to properly develop and extend it.

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HIFUN is a high-level query language for expressing analytic queries over big data sets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer where queries are evaluated. In this paper, we present a methodology, based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach, is able to process the most recent data batch, exploiting already computed information, without requiring the evaluation of the query over the complete data set. We present the generic algorithm which subsequently, we translate to both SQL and MapReduce using SPARK, implementing various query rewriting methods. We demonstrate the effectiveness of our approach in achieving query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

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Sentiment Analysis is an actively growing field with demand in both scientific and industrial sectors. Political sentiment analysis is used when a data analyst wants to determine the opinion of different users on social media platforms regarding a politician or a political event. This paper presents Athena Political Popularity Analysis (AthPPA), a tool for identifying political popularity over Twitter. AthPPA is able to collect in real-time tweets and for each tweet to extract metadata such as number of likes, retweets per tweet etc. Then it processes their text in order to calculate their overall sentiment. For the calculation of sentiment analysis, we have implemented a sentiment analyzer that is able to identify the grammatical issues of a sentence as well as a lexicon of negative and positive words designed specifically for political sentiment analysis. An analytic engine processes the collected data and provides different visualizations that provide additional insights on the collected data. We show how we applied our framework to the three most prominent Greek political leaders in Greece and present our findings there.

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Advances in computers and communications have significantly changed almost every aspect of our daily activity. In this maze of change, governments around the world cannot remain indif-ferent. Public administration is evolving and taking on a new form through e-Government. A large number of organizations have set up websites, establishing an online interface with the citizens and businesses with which it interacts. However, most organizations, especially the de-centralized agencies of the ministries and local authorities, despite the fact that they provide many information services they are not integrated with other e-government services and are not offered electronically. Besides, these services are mainly focused on serving citizens and busi-nesses and less on providing services to employees. In this paper, we describe the process of de-veloping an Ontology to support the administrative procedures of decentralized government organizations. Finally, we describe the development of an e-government portal that provides employees services that are processed online, using the above ontology for modeling and data management.

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The lives of millions of people have been affected during the coronavirus pandemic that spread throughout the world in 2020. Society is changing establishing new norms for healthcare education, social life, and business. Digital health has seen an accelerated implementation throughout the world in response to the pandemic challenges. In this perspective paper, the authors provide an overview of the digital platform Safe in COVID-19 to highlight the necessary requirements for incorporating digital apps in integrated healthcare systems. Semantics and data management could highly contribute, along with personal health apps, to the secondary use of available data in order to support effective prevention, prediction, and disease management, while at the same time increase collective knowledge.

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The advancements in health-care have brought to the foreground the need for flexible access to health-related information and created an ever-growing demand for efficient data management infrastructures. To this direction, many challenges must be first overcome, enabling seamless, effective and efficient access to several health data sets and novel methods for exploiting the existing information. The second international workshop on semantic Web technologies for health data management aimed at putting together an interdisciplinary audience that is interested in the fields of semantic web, data management and health informatics to discuss the challenges in health-care data management and to propose new solutions for the next generation data-driven health-care systems. In this article, we summarize the outcomes of the workshop, and we present a number of key observations and research directions that emerge.

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Sentiment analysis over social media platforms has been an active case of study for more than a decade. This occurs due to the constant rising of internet users over these platforms as well as to the increasing interest of companies for monitoring the opinion of customers over commercial products. Most of these plat-forms provide free online services such as the creation of interactive web communities, multimedia content uploading etc. This new way of communication has affected human societies as it shaped the way by which an opinion can be expressed, sparking the era of digital revolution. One of the most profound examples of social networking platforms for opinion mining is Twitter as it is a great source for extracting news and a platform which politicians tend to use frequently. In addition to that, the character limitation per posted tweet (maximum of 280 characters) makes it easier for automated tools to extract its underlying sentiment. In this review paper, we present a variety of lexicon-based tools as well as machine learning algorithms used for sentiment extraction. Furthermore, we present additional implementations used for political sentiment analysis over Twitter as well as additional open topics. We hope the review will help readers to under-stand this scientifically rich area, identify best options for their work and work on open topics.

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Background: Patients undergoing elective surgery often face symptoms of anxiety and stress. Healthcare systems have limited time and resources to provide individualized stress relief interventions. Research has shown that stress relief interventions and educational resources can improve health outcomes and speed recovery. Objectives: Digital health tools can provide valuable assistance in stress relief and educational support to patients and family. This paper reports on the design of a novel digital health infrastructure for improving the health condition of patients, during the care path, using virtual reality (VR) and other information and communication technologies (ICT). Methods: Digital tools developed and integrated into a platform of modules that can be used by patients before but also after an operation, enabling better self-management and self-empowerment. Results: The designed platform aims at improving the knowledge of patients about their condition, providing stress relief tools, helping them adhere to treatment, as well as providing for effective communication channels between patients and clinicians. Conclusion: The proposed solution has the potential to improve physical and emotional reactions to stress and increase the levels of calmness and a sense of wellbeing. Information provided through the platform enhances health literacy and digital competence, and increases the participation of the patient in the decision-making process. Integration with third-party applications can facilitate the exchange of important information between patients and physicians as well as between personal applications and clinical health systems.

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Background: A vast amount of mobile apps have been developed during the past few months in an attempt to “flatten the curve” of the increasing number of COVID-19 cases. Objective: This systematic review aims to shed light into studies found in the scientific literature, which have used and evaluated mobile apps for the prevention, management, treatment, and/ or follow-up of COVID-19. Methods: We searched the bibliographic databases of COVID-19 global literature on coronavirus disease, PubMed and Scopus, to identify papers focusing on mobile apps for COVID-19 that (i) show evidence on their real-life use and (ii) have been developed involving clinical professionals in their design or validation. Results: Mobile apps have been implemented for training, information sharing, risk assessment, self-management of symptoms, contact tracing, home-monitoring and decision making, rapidly offering effective and usable tools against the COVID-19 pandemic. Conclusions: Mobile apps are considered to be a valuable tool for citizens, health professionals and decision makers in facing critical challenges imposed by the pandemic, such as reducing the burden of hospitals, providing access to credible information, tracking symptoms and the mental health of individuals, and discovering new predictors of COVID-19.

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Background: This article discusses the status, the challenges, the opportunities and the recommendations aimed at accelerating the rate of progress in the data driven management of cancer care. Two international workshops in June 2019, in Cordoba, Spain and then in October 2019, in Athens, Greece were organized by four HORIZON2020 EU funded projects: BOUNCE, CATCH ITN, DESIREE and MyPal. The issues covered include patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, interoperability of ICT platforms and others. A series of recommendations is provided as the complex landscape of data-driven technical innovation in cancer care is being portrayed. Objective: Provide information on the current state of the art of technology and data-driven innovation for management of cancer care through the work of four EU H2020 funded projects. Methods: Reporting of the results of two international workshops on ICT in management of cancer care. Results: Technical and data-driven innovation provide promising tools for the management of cancer care. However, several challenges need to be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, trust and others. The paper analyses these challenges, which are seen as opportunities for further research and practical implementation and provides practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In the process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem to fully benefit from what these innovations have to offer.

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Chatbots, also known as conversation agents, are programs being able to simulate and reproduce an intelligent conversation with humans. Although this type of programs is not new, the explosion of the available information and the rapid increase of the users seeking for this information, has renewed the interest for their development. In this paper, we present R2D2 an intelligent chatbot, relying on semantic web technologies to enable intelligent question answering over the information available in DBPedia. The chatbot accepts structured input, allowing users to enter triple-pattern like queries, which are being answered by the underlying engine. While typing, an auto-complete service guides users on creating the triple-patterns, suggesting resources available in the DBPedia. Based on user input, the corresponding SPARQL queries are automatically formulated. The queries are submitted to the corresponding DBPedia SPARQL endpoint, the result is received by R2D2 and augmented with maps and visuals and eventually presented to the user. The usability evaluation performed shows the advantages of our solution and its usefulness.

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Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group-dynamics-based principles of behavior change have a positive effect on the patients' welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy and the psycho-emotional status of the patients. Exploiting those dimensions we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach is able to perform better recommendations to small group of patients for useful information documents.

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Over the last decade, there have been many changes in the field of political analysis at a global level. Through social networking platforms, millions of people have the opportunity to express their opinion and capture their thoughts at any time, leaving their digital footprint. As such, massive datasets are now available, which can be used by analysts to gain useful insights on the current political climate and identify political tendencies. In this paper, we present TwiFly, a framework built for analyzing Twitter data. TwiFly accepts a number of accounts to be monitored for a specific time-frame and visualizes in real time useful extracted information. As a proof of concept, we present the application of our platform to the most recent elections of Greece, gaining useful insights on the election results.

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As a result of recent advances in cancer research and “precision medicine” approaches, i.e. the idea of treating each patient with the right drug at the right time, more and more cancer patients are being cured, or might have to cope with a life with cancer. For many people, cancer survival today means living with a complex and chronic condition. Surviving and living with or beyond cancer requires the long-term management of the disease, leading to a significant need for active rehabilitation of the patients. In this paper, we present a novel methodology employed in the iManageCancer project for cancer patient empowerment in which personal health systems, serious games, psychoemotional monitoring and other novel decision-support tools are combined into an integrated patient empowerment platform. We present in detail the ICT infrastructure developed and our evaluation with the involvement of cancer patients on two sites, a large-scale pilot for adults and a small-scale test for children. The evaluation showed mixed evidences on the improvement of patient empowerment, while ability to cope with cancer, including improvement in mood and resilience to cancer, increased for the participants of the adults’ pilot.

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A huge amount of data is generated each day from various sources. Analysis of these massive data is difficult, and requires new forms of processing to enable enhanced decision making, in-sight discovery and process optimization. In addition, besides their ever increasing volume, da-tasets change frequently, and as such, results to continuous queries have to be updated at short intervals. In this paper, we address the problem of evaluating continuous queries over big data streams that are frequently updated, adopting HIFUN, a high-level query language introduced recently. HIFUN offers a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer where queries are evaluated, by encoding them as map-reduce jobs or as SQL group-by queries. Using HIFUN, we devise an algorithm for incremental processing of continuous queries, processing only the most recent data partition, and exploiting already computed information, without requiring evalu-ating the query over the complete dataset. Subsequently, we translate the generic algorithm to both SQL and MapReduce using SPARK, exploiting the query rewriting method provided by HIFUN. The experiments performed show the advantages of our solution in terms of query answering efficiency.

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The early and accurate identification of a disease is important for its effective treatment. However, medical errors represent a serious problem and pose a threat to patient safety. To this direction, appropriate and continuous education of the medical personnel has been widely recognized as an important mean to reduce medical errors and increase the quality of the health system. In this paper, we present MediExpert, an expert system targeting on continuous and remote/online education of health personnel, providing also guidelines to persons that either cannot easily move due to age related comorbidities, or because they are away from healthcare units, further recommending users to talk with their doctors. It is based on differential diagnosis, employs ontologies for effective classification of health related problems and intelligent algorithms to enhance continuous education, offering as such a useful tool to both health providers and patients, allowing educating them at their own pace and location. We present the various components of the system and we elaborate on the benefits gained when using it for education.

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Many modern applications produce massive streams of data series that need to be analyzed, requiring efficient similarity search operations. However, the state-of-the-art data series indexes that are used for this purpose do not scale well for massive datasets in terms of performance, or storage costs. We pinpoint the problem to the fact that existing summarizations of data series used for indexing cannot be sorted while keeping similar data series close to each other in the sorted order. This leads to two design problems. First, traditional, efficient algorithms for bulk-loading and updating an index cannot be used because they rely on sorting. Instead, index construction and updates takes place through slow top-down insertions, which create a non-contiguous index that results in many random I/Os. This also prevents an index from maintaining temporal partitions of the data to support window queries, as the cost of merging temporal partitions via top-down insertions (rather than through sorting) is prohibitive. Second, data series cannot be sorted and split across nodes evenly based on their median value; thus, most leaf nodes are in practice nearly empty. This further slows down query speed and amplifies storage costs. To address these problems, we present Coconut, the first data series index based on sortable summarizations, and the first efficient solution for indexing and querying streaming series. The first innovation in Coconut is an inverted, sortable data series summarization that organizes data series based on a z-order curve, keeping similar series close to each other in the sorted order. As a result, Coconut is able to use bulk-loading and updating techniques that rely on sorting to quickly build and maintain a contiguous index using large sequential disk I/Os. We then explore prefix-based and median-based splitting policies for bottom-up bulkloading, showing that median-based splitting outperforms the state of the art, ensuring that all nodes are densely populated. Finally, we explore the impact of sortable summarizations on variable size window queries, showing that they can be supported in the presence of updates through efficient merging of temporal partitions. Overall, we show analytically and empirically that Coconut dominates the state-of-theart data series indexes in terms of construction speed, query speed, and storage costs

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Background: Mobile health (mHealth) technology has the potential to play a key role in improving the health of patients with chronic non-communicable diseases. Objectives: We present a review of systematic reviews of mHealth in chronic disease management, by showing the features and outcomes of the studied interventions, along with associated challenges in this rapidly growing field. Methods: We searched the bibliographic databases of PubMed, Scopus, and Cochrane to identify systematic reviews of mHealth interventions with advanced technical capabilities (e.g., Internet-linked apps, interoperation with sensors, communication with clinical platforms, etc.) utilized in randomized clinical trials. Reviewed studies were synthesized according to their intervention features, the targeted diseases, the primary outcome, the number of participants and their average age, as well as the total follow-up duration. Results: We identified 5 reviews respecting our inclusion and exclusion criteria, which examined 30 mHealth interventions. The highest percentage of the interventions targeted patients with diabetes (n=19, 63%), followed by patients with psychotic disorders (n=7, 23%), lung diseases (n=3, 10%), and cardiovascular disease (n=1, 3%). 14 studies showed effective results: 9 in diabetes management, 2 in lung function, and 3 in mental health. Significantly positive outcomes were reported in 8 interventions (n=8, 47%) from 17 studies assessing glucose concentration, one intervention assessing physical activity, 2 interventions (n=2, 67%) from 3 studies assessing lung function parameters, and 3 mental health interventions assessing N-back performance, medication adherence, and number of hospitalizations. Divergent features were adopted in 14 interventions with significantly positive outcomes, such as personalized goal setting (n=10, 71%), motivational feedback (n=5, 36%), and alerts for caregivers health professionals (n=3, 21%). The most significant found challenges in the development and evaluation of mHealth interventions include the design of studies with high quality, the construction of robust standalone interventions and in combination with health professional inputs, and the identification of tools and methods to improve patient adherence. Conclusions: This review found mixed evidence regarding the health benefits of mHealth interventions for patients living with chronic diseases. Further rigorous studies are needed to assess the outcomes of personalized mHealth interventions toward the optimal management of chronic diseases.

Abstract

The huge diversity and quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and reasoning This paper elaborates on the design and development of COSMOS, an intelligent system which supports a) collaboration of experts for developing common knowledge bases and b) diagnosis derivation which takes into account time and uncertainty. The health domain is used for illustration and discussion of the features of our approach. Initially, we present the syntax and the semantics of the rules which incorporate time (temporal rules) and the data items upon which reasoning is performed. Then we introduce its inference engine, able to perform reasoning on top of rules and data, handling also the embedded time and uncertainty. We proceed further to define a conflict detection policy for supporting the difficult and error prone task of rule generation. The complexities of the aforementioned tasks are hidden from users via a well-designed and user friendly web interface that possesses strong collaboration features enabling multiple experts to work on defining a rule and on developing a common knowledge base. Evaluation of COSMOS has been performed using a) students studying expert systems and b) health experts in order to demonstrate the usability of the approach and the considerable advantages gained. To the best of our knowledge, COSMOS is one of the very few systems combining temporal rules, a powerful inference engine handling uncertainty and conflict detection and progresses beyond state of the art by adopting strong collaboration features and paradigms.

Abstract

Anxiety and stress are very common symptoms of patients facing a forthcoming surgery. However, limited time and resources within healthcare systems make the provision of stress relief interventions difficult to provide. Research has shown that provision of preoperative stress relief and educational resources can improve health outcomes and speed recovery. Information and Communication Technology (ICT) can be a valuable tool in providing stress relief and educational support to patients and family before but also after an operation, enabling better self-management and self-empowerment. To this direction, this paper reports on the design of a novel technical infrastructure for a resilience support tool for improving the health condition of patients, during the care path, using Virtual Reality (VR). The designed platform targets, among others, at improving the knowledge on the patient data, effectiveness and adherence to treatment, as well as providing for effective communication channels between patients and clinicians.

Abstract

Better information management is the key to a more intelligent health and social system. To this direction, many challenges must be first overcome, enabling seamless, effective and efficient access to the various health data sets and novel methods for exploiting the available information. The First International Workshop on Semantic Web Technologies for Health Data Management aimed at bringing together an interdisciplinary audience interested in the fields of semantic web, data management and health informatics to discuss the unique challenges in health-care data management and to propose novel and practical solutions for the next generation data-driven health-care systems. In this paper we summarize the outcomes of the first instance of the workshop, and we present interesting conclusions and key messages..

Abstract

Many modern applications produce massive amounts of data series that need to be analyzed, requiring effiient similarity search operations. However, the state-of-the-art data series indexes that are used for this purpose do not scale well for massive datasets in terms of performance, or storage costs. We pinpoint the problem to the fact that existing summarizations of data series used for indexing cannot be sorted while keeping similar data series close to each other in the sorted order. This leads to two design problems. First, traditional bulk-loading algorithms based on sorting cannot be used. Instead, index construction takes place through slow top-down insertions, which create a non-contiguous index that results in many random I/Os. Second, data series cannot be sorted and split across nodes evenly based on their median value; thus, most leaf nodes are in practice nearly empty. This further slows down query speed and amplifis storage costs. To address these problems, we present Coconut. The first innovation in Coconut is an inverted, sortable data series summarization that organizes data series based on a z-order curve, keeping similar series close to each other in the sorted order. As a result, Coconut is able to use bulk-loading techniques that rely on sorting to quickly build a contiguous index using large sequential disk I/Os. We then explore prefix-based and median-based splitting policies for bottom-up bulk-loading, showing that median-based splitting outperforms the state of the art, ensuring that all nodes are densely populated. Overall, we show analytically and empirically that Coconut dominates the state-of-the-art data series indexes in terms of construction speed, query speed, and storage costs.

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Abstract

Chronic pain is one of the most common health problems affecting daily activity, employment, relationships and emotional functioning. Unfortunately, limited access to pain experts, the high heterogeneity in terms of clinical manifestation and treatment results, contribute in failure to manage efficiently and effectively pain. Information and Communication Technology (ICT) can be a valuable tool, enabling better self-management and self-empowerment of pain. To this direction, this paper reports on the design of a novel technical infrastructure for chronic pain self-management based on an Intelligent Personal Health Record platform. The designed platform targets, among others, at improving the knowledge on the patient data, effectiveness and adherence to treatment and providing effective communication channels between patients and clinicians.

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Abstract

Ontologies are constantly evolving as new requirements daily occur and the modeling choices of the past should be updated or adapted. Rapidly understanding and exploring this evolution will enable the ontology engineers to understand the modeling choices of the past and maintainers of the depending artifacts to make decisions about possible changes. However, recent research focuses only on detecting changes between ontology versions, overloading end-users with hundreds or even thousands of changes between ontology versions, making it impossible to explore what has happened. To this direction, in this paper, we present EvoRDF, a novel framework for exploring ontology evolution using provenance queries. Our approach uses a high-level language of changes and effectively answers queries about when a specific resource and how – by which change operations. Even more, why queries can identify the sequence of changes that led to the creation of a specific resource in the latest ontology version or track the evolution of a specific resource from a past ontology version. The evaluation performed shows the feasibility of our solution and the great advantages gained.

Abstract

Developments in information and communication technology have changed the way healthcare processes are experienced by both patients and healthcare professionals: more and more services are now available through computers and mobile devices. Smartphones are becoming useful tools for managing one’s health, and today, there are many available apps meant to increase self-management, empowerment and quality of life. However, there are concerns about the implications of using mHealth and apps: data protection issues, concerns about sharing information online, and the patients’ capacity for discerning effective and valid apps from useless ones. The new general data protection regulation has been introduced in order to give uniformity to data protection regulations among European countries but shared guidelines for mHealth are yet to develop. A unified perspective across Europe would increase the control over mHealth exploitation, making it possible to think of mHealth as effective and standard tools for future medical practice.

Abstract

Patients today have a wealth of information available on the internet. Despite, the potential benefits of internet health information seeking, several concerns have been raised about the quality of in-formation and about the patient’s capability to evaluate medical information and to relate it to their own disease and treatment. As such, novel tools are required able to effectively guide patients and provide high quality medical information in an intelligent and personalized manner. To this direction, this paper presents a platform, trying to empower patients by enabling them to search in a high quality document repository selected by experts, avoiding the information overload of the internet. In addition, the information provided to the patients is personalized, based on indi-vidual preferences, medical conditions and other profiling information. Despite the generality of our approach, we apply our solution to a Personal Health Record constructed for the cancer patients and we report our initial findings. To the best of our knowledge, our platform is the only one combin-ing advanced natural language processing, ontologies and personal information to offer a unique user experience.

Abstract

n the last decade, clinicians have started to shift from an individualistic perspective of the patient towards family-centred models of care, due to the increasing evidence from research and clinical practice of the crucial role of significant others in determining the patient's adjustment to cancer disease and management. eHealth tools can be considered a means to compensate the services gap and support outpatient care flows. Within the works of the European H2020 iManageCancer project, a review of the literature in the field of family resilience was conducted, in order to determine how to monitor the patient and his/her family's resilience through an eHealth platform. An analysis of existing family resilience questionnaires suggested that no measure was appropriate for cancer patients and their families. For this reason, a new family resilience questionnaire (named FaRe) was developed to screen the patient's and caregiver's psycho-emotional resources. Composed of 24 items, it is divided into four subscales: Communication and Cohesion, Perceived Family Coping, Religiousness and Spirituality, and Perceived Social Support. Embedded in the iManageCancer eHealth platform, it allows users and clinicians to monitor the patient's and the caregivers' resilience throughout the cancer trajectory.

Abstract

Clinical decision support systems can play a crucial role in healthcare delivery as they promise to improve health outcomes and patient safety, reduce medical errors and costs and contribute to patient satisfaction. Used in an optimal way, they increase the quality of healthcare by proposing the right information and intervention to the right person at the right time in the healthcare delivery process. This paper reports on a specific approach to integrated clinical decision support and patient guidance in the cancer domain as proposed by the H2020 iManageCancer project. This project aims at facilitating efficient self-management and management of cancer according to the latest available clinical knowledge and the local healthcare delivery model, supporting patients and their healthcare providers in making informed decisions on treatment choices and in managing the side effects of their therapy. The iManageCancer platform is a comprehensive platform of interconnected mobile tools to empower cancer patients and to support them in the management of their disease in collaboration with their doctors. The backbone of the iManageCancer platform comprises a personal health record and the central decision support unit (CDSU). The latter offers dedicated services to the end users in combination with the apps iManageMyHealth and iSupportMyPatients. The CDSU itself is composed of the so-called Care Flow Engine (CFE) and the model repository framework (MRF). The CFE executes personalised and workflow oriented formal disease management diagrams (Care Flows). In decision points of such a Care Flow, rules that operate on actual health information of the patient decide on the treatment path that the system follows. Alternatively, the system can also invoke a predictive model of the MRF to proceed with the best treatment path in the diagram. Care Flow diagrams are designed by clinical experts with a specific graphical tool that also deploys these diagrams as executable workflows in the CFE following the Business Process Model and Notation (BPMN) standard. They are exposed as services that patients or their doctors can use in their apps in order to manage certain aspects of the cancer disease like pain, fatigue or the monitoring of chemotherapies at home. The mHealth platform for cancer patients is currently being assessed in clinical pilots in Italy and Germany and in several end-user workshops.

Abstract

The ever-growing demand for acquiring, managing and exploiting patient health related information has led to the development of many e-Health systems and applications. However, despite the number of systems already developed and the apparent need for such systems, end users can only collect online and exploit, only a limited set of information for health purposes in the context of personalized, preventive and participatory medicine. To this direction, this paper initially presents the personal health record (PHR) concept, related work and best practices for the development of PHR systems in a standardized manner. It also outlines the proposal for meaningful use criteria in the United States (US) and the health level seven (HL7) personal health record system functional model (PHR-S FM). Focus is put on trying to link core functionality modules of the Integrated Care Solutions TM PHR system, designed to support the citizen, paying emphasis on wellbeing, home care and the management of chronic diseases with PHR-S FM personal health functions, in a preliminary effort towards the exploration of functional models to support interoperability. Based on accumulated experiences from many European Union (EU) research projects, the paper concludes by providing directions towards achieving wider PHR adoption and meaningful use.

Abstract

Purpose. Biomedical research is being catalyzed by the vast amount of data rapidly collected through the application of information technologies (IT). Despite IT advances, the methods for involving patients and citizens in biomedical research remain static, paper-based and organized around national boundaries and anachronistic legal frameworks. The purpose of this paper is to study the current situation in the European Union and identify the requirements for building effective IT systems enabling the secondary use of data and biomaterial. Method. We review existing European legislation for secondary use of patient’s biomaterial and data for research, identify types and scopes of consent, formal requirements for consent, and consider their implications for implementing electronic consent tools. Results. In the light of our analysis of the European legislation requirements, we identified preconditions for building an effective tool enabling secondary usage of both patient’s data and biomaterial. We proceeded to develop a modular tool, named Donor’s Support Tool (DST), designed to connect researchers with participants, and to promote engagement, informed participation and individual decision making. As a proof of concept, we then deployed the tool in a real clinical environment; here we report our experiences showing the significant advantages and the potential of our approach.

Abstract

Today we are witnessing an explosion in the size and the amount of the available RDF datasets. As such, conventional single node RDF management systems give their position to clustered ones. However most of the currently available clustered RDF database systems partition data using hash functions and/or vertical and horizontal partition algorithms with a significant impact on the number of nodes required for query answering, increasing the total cost of query evaluation. In this paper we present a novel semantic partitioning approach, exploiting both the structure and the semantics of an RDF Dataset, for producing vertical partitions that significantly reduce the number of nodes that should be visited for query answering. To construct these partitions, first we select the most important nodes in a dataset as centroids, using the notion of relevance. Then we use the notion of dependence to assign each remaining node to the appropriate centroid. We evaluate our approach using three real world datasets and demonstrate the nice properties that the constructed partitions possess showing that they significantly reduce the total number of nodes required for query answering while introducing minimal storage overhead.

Abstract

Given the explosive growth in both data size and schema complexity, data sources are becoming increasingly difficult to use and comprehend. Summarization aspires to produce an abridged version of the original data source highlighting its most representative concepts. In this paper, we present an advanced version of the RDF Digest, a novel platform that automatically produces and visualizes high quality summaries of RDF/S Knowledge Bases (KBs). A summary is a valid RDFS graph that includes the most representative concepts of the schema, adapted to the corresponding instances. To construct this graph we designed and implemented two algorithms that exploit both the structure of the corresponding graph and the semantics of the KB. Initially we identify the most important nodes using the notion of relevance. Then we explore how to select the edges connecting these nodes by maximizing either locally or globally the importance of the selected edges. The extensive evalua-tion performed compares our system with two other systems and shows the benefits of our approach and the considerable advantages gained.

Abstract

The objective of the INTEGRATE project (http://www.fp7-integrate.eu/) that has recently concluded successfully was the development of innovative biomedical applications focused on streamlining the execution of clinical research, on enabling multidisciplinary collaboration, on management and large-scale sharing of multi-level heterogeneous datasets, and on the development of new methodologies and of predictive multi-scale models in cancer. In this paper, we present the way the INTEGRATE consortium has approached important challenges such as the integration of multi-scale biomedical data in the context of post-genomic clinical trials, the development of predictive models and the implementation of tools to facilitate the efficient execution of postgenomic multi-centric clinical trials in breast cancer. Furthermore, we provide a number of key “lessons learned” during the process and give directions for further future research and development.

Abstract

Background: The European eHealthMonitor project (eHM) developed a user-sensitive and interactive web portal for the dementia care setting: The eHM Dementia Portal (eHM-DP). It aims to provide targeted support for informal caregivers of persons with dementia and professionals.
Objective: The objective of this study was to assess the usefulness and impact of the eHM-DP service in the dementia care setting from two user perspectives: informal caregivers and professionals.
Methods: The evaluation study was conducted from June to September 2014 and followed a before-after, user-participatory, mixed-method design with questionnaires and interviews. The used intervention was the eHM-DP: an interactive web portal for informal caregivers and professionals that was tested for a 12-week period. Primary outcomes for caregivers included: empowerment, quality of life, caregiver burden, decision aid as well as perceived usefulness and benefits of the eHM-DP. Primary outcomes for professionals involved decision aid as well as perceived usefulness and benefits of the eHM-DP.
Results: A total number of 25 informal caregivers and 6 professionals used the eHM-DP over the 12 weeks study period. Both professionals and informal caregivers indicated perceived benefits and support by the eHM-DP. In total 65% (n=16) of informal caregivers would use the eHM-DP if they had access to it. Major perceived benefits (65%; n=16 of informal caregivers) were individualized information acquisition, improved interaction between informal caregivers and professionals, access to support from home and empowerment in health related decisions (PrepDM Score: 67.9). Professionals highlighted the improved treatment and care over the disease course (83%, n=5) and improved health care access for people living in rural areas (67%; n=4). However, there was no improvement in caregiver burden (BSFC) and quality of life (EQ-5D-5L) over the study period.
Conclusions: Our study provides insight into the different user perspectives on an eHealth support service in the dementia treatment and care setting. The presented results are of importance for future developments and the uptake of eHealth solutions in the dementia domain and reinforce the importance of early user involvement. Turning to the primary target of the eHM-DP service, the presented findings suggest that the eHM-DP service proved to be a valuable post-diagnostic support service, in particular for the homebased care setting. Further research on a larger scale is needed to enhance the implementation in existing health care infrastructures.

Abstract

Background:
The uniqueness of a patient as determined by the integration of clinical data and psychological aspects should be the aspired aim of a personalized medicine approach. Nevertheless, given the time constraints usually imposed by the clinical setting, it is not easy for the physicians to collect information about the patient’s unique mental dimensions and needs related to their illness. Such information may be useful to tailor patient-physician communication improving the patient’s understanding of provided information, her involvement in the treatment process and, in general, her empowerment during and after the therapeutic journey. The primary objective of this study is to evaluate the effect of an interactive empowerment tool (IEm) on enhancing the breast cancer patient-physician experience, in terms of empowerment increasing, by providing physicians a personalized patient’s profile, accompanied by specific recommendations to suggest them how to interact with each single patient on the basis of her personal profile. Methods:
The study will be implemented as a 2-arm randomized controlled trial with 100 adult breast cancer patients who fill in the ALGA-BC questionnaire, a computerized validated instrument to evaluate the patient’s physical and psychological characteristics following a breast cancer diagnosis. The IEm tool will collect and analyze the patient’s answers in real time and send them, together with specific recommendations, to the physician’s computer immediately before his/her first encounter with the patient. Patients will be randomized to either the intervention group using the IEm tool or to a control group who will only fill the questionnaire without taking the advantages of the tool (physicians will not receive the patient’s profile). Our primary outcome, patient empowerment, will be assessed by t-test for independent groups. Effect sizes will be calculated as mean group differences with standard deviations. Discussion:
The proposed approach is supposed to improve the patient-physician communication leading to an increased patient’s participation in the therapeutic process with a consequent improvement in patient empowerment and personalization of care.

Abstract

The aggregation of heterogeneous data from different institutions in cultural heritage and e-science has the potential to create rich data resources useful for a range of different purposes, from research to education and public interests. In this paper, we present the X3ML framework, a framework for information integration that handles effectively and efficiently the steps involved in schema mapping, Unique Resource Identifier (URI) definition and generation, data transformation, provision and aggregation. The framework is based on the X3ML mapping definition language for describing both schema mappings and URI generation policies and has a lot of advantages when compared to other relevant frameworks. We describe the architecture of the framework as well as details on the various available components. Usability aspects are discussed and performance metrics are demonstrated. The high impact of our work is verified via the increasing number of international projects that adopt and use this framework.

Abstract

The advent of new web technologies and the explosion of available information online led to an information overload. During this information revolution blogs have become considerably mainstream as a media of providing news. Although there are several arguments about their validity and credibility the large amount of blogs currently available require the usage of advanced techniques for the collection, analysis, mining and efficient querying of the available information. To this direction we present BlogSearch a novel platform allowing aggregating, indexing and searching blog articles. The information is modelled using a novel RDF/S Ontology named Blogs Ontology and published as Linked Open Data. In addition, two set of APIs are provided for inserting, updating and searching information whereas the platform provides also graphical user interfaces (GUIs) for searching and inserting information. To the best of our knowledge our platform is the only one currently available publishing blog articles as Linked Open Data and simultaneously providing API’s and GUIs for aggregating, inserting and searching articles.

Abstract

Personalized medicine should target not only the genetic and clinical aspects of the individual patients but also the different cognitive, psychological, family and social factors involved in various clinical choices. To this direction, in this paper, we present instruments to assess the psycho-emotional status of cancer patients and to evaluate the resilience in their family constructing in such a way an augmented patient profile. Using this profile, a decision aid can increase patient’s participation in the consultation process with their physicians and improve their satisfaction and involvement in the decision-making process. The decision aid provides standardized sets of questions related to patient’s condition and choices and the patient can choose or create his own lost to further discuss with his doctor. Our preliminary evaluation of shows promising results and the potential benefits of the tools.

Abstract

Information of the healthcare domain and especially the personal health records is characterized by its heterogeneity. Clinical, lifestyle, environmental data and personal preferences are stored and managed within such platforms. Data of such a diverse information space is difficult to be delivered, especially to non-IT users like physicians or managers. Another obstacle of the health data management and analysis is the volume, which increases more and more making the need for efficient visualization methods of data and data analysis results mandatory. The objective of this work is to present architectural design recommendations of an environment for the seamless integration and intelligent processing of distributed and heterogeneous clinical information. Such a system will ease health-care professionals to orient themselves in the disperse information space and enhance their decision-making capabilities.

Abstract

Background. As cancer survival increases, a new partnership model that empowers patients and caregivers and promotes self-management of the disease is needed. The H2020 iManageCancer project aims to respond to the aforementioned needs with a cancer specific personalized platform. This abstract describes the development of two tools to: 1) monitor the psycho-emotional status of the patients, and 2) evaluate family resilience in cancer, as part of the activities for the project. Methods: To identify the requirements for the tools, a review on patient empowerment, psycho-cognitive constructs relevant in cancer treatments, and family resilience in (cancer) disease was performed, together with an analysis based on patients interviews. Additionally, an online survey was promoted to the cancer community. On this basis, two use case scenarios were created to develop the tools. Results: A complex structure emerged as relevant in the assessment of the psycho-emotional status of cancer patients: psychosocial aspects, cognitive aspects, perceived health state, decisional preferences and role, perceived control of the disease, emotional profile, and patient engagement. For the assessment of family resilience, communication and problem solving, social and economic resources, connectedness and spirituality, and reappraisal emerged as important aspects. Conclusions: To the best of our knowledge these tools within the broader iManageCancer platform represent the the first approach combining psycho-emotional and resilience monitoring of the individual patient and his family, to enable patient empowerment end strengthen patient-doctor communication.

Abstract

Mode analysis in logic programs has been used mainly for code optimization. The mode analysis in this paper supports the program construction process. It is applied to partially complete logic programs. The program construction process is based on schema refinements and refinements by data type operations. Refinements by data type operations are at the end of the refinement process. This mode analysis supports the proper application of refinements by data type operations. In addition, it checks that the declared modes as defined by the Data Type (DT) operations are consistent with the inferred runtime modes. We have implemented an algorithm for mode analysis based on minimal function graphs. An overview of our logic program development method and the denotational semantics of the analysis framework are presented in this paper.

Abstract

An overview is presented on the data management architecture of iManageCancer.

Abstract

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of SemanticWeb techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety ofelds (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an indepth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, werst provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

Abstract

Personal health record (PHR) systems are a rapidly expanding area in the field of health information technology which motivates an ongoing research towards their evaluation in several different aspects. In this direction, we present a systematic review of the currently available PHR systems. Initially, we define a clear and concise set of requirements for efficient PHR systems which is based on real-world implementation experiences of several European research projects and also on established and widely used formal standards. Subsequently, these requirements are used to perform a systematic evaluation of existing PHR system implementations. Our evaluation study provides a thorough requirement analysis and an insight on the current status of personal health record systems. The results of the present work can therefore be used as a basis for future evaluation studies which should be conducted periodically as technology evolves and requirements are revised.