Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Cognitive Computing supported Medical Decision Support System for Patient’s Driving Assessment

To smartly utilize a huge and constantly growing volume of data, improve productivity and increase competitiveness in various fields of life; human requires decision making support systems that efficiently process and analyze the data, and, as a result, significantly speed up the process. Similarly to all other areas of human life, healthcare domain also is lacking Artificial Intelligence (AI) based solution. A number of supervised and unsupervised Machine Learning and Data Mining techniques exist to help us to deal with structured data. However, in a real life, we pretty much deal with unstructured data that hides useful knowledge and valuable information inside human-readable plain texts, images, audio and video. Therefore, such IT giants as IBM, Google, Microsoft, Intel, Facebook, etc., as well as variety of SMEs are actively elaborating different Cognitive Computing services and tools to get a value from unstructured data. Thus, the paper presents feasibility study of IBM Watson cognitive computing services and tools to address the issue of automated health records processing to support doctor’s decision for patient’s driving assessment

Developing an infrastructure for secure patient summary exchange in the EU context: Lessons learned from the KONFIDO project:

Background: The increase of healthcare digitalization comes along with potential information security risks. Thus, the EU H2020 KONFIDO project aimed to provide a toolkit supporting secure cross-border health data exchange. Methods: KONFIDO focused on the so-called "User Goals", while also identifying barriers and facilitators regarding eHealth acceptance. Key user scenarios were elaborated both in terms of threat analysis and legal challenges. Moreover, KONFIDO developed a toolkit aiming to enhance the security of OpenNCP, the reference implementation framework. Results: The main project outcomes are highlighted and the "Lessons Learned," the technical challenges and the EU context are detailed. Conclusions: The main "Lessons Learned" are summarized and a set of recommendations is provided, presenting the position of the KONFIDO consortium toward a robust EU-wide health data exchange infrastructure. To this end, the lack of infrastructure and technical capacity is highlighted, legal and policy challenges are identified and the need to focus on usability and semantic interoperability is emphasized. Regarding technical issues, an emphasis on transparent and standards-based development processes is recommended, especially for landmark software projects. Finally, promoting mentality change and knowledge dissemination is also identified as key step toward the development of secure cross-border health data exchange services

Developing an infrastructure for secure patient summary exchange in the EU context: Lessons learned from the KONFIDO project

Background: The increase of healthcare digitalization comes along with potential information security risks. Thus, the EU H2020 KONFIDO project aimed to provide a toolkit supporting secure cross-border health data exchange. Methods: KONFIDO focused on the so-called “User Goals”, while also identifying barriers and facilitators regarding eHealth acceptance. Key user scenarios were elaborated both in terms of threat analysis and legal challenges. Moreover, KONFIDO developed a toolkit aiming to enhance the security of OpenNCP, the reference implementation framework. Results: The main project outcomes are highlighted and the “Lessons Learned,” the technical challenges and the EU context are detailed. Conclusions: The main “Lessons Learned” are summarized and a set of recommendations is provided, presenting the position of the KONFIDO consortium toward a robust EU-wide health data exchange infrastructure. To this end, the lack of infrastructure and technical capacity is highlighted, legal and policy challenges are identified and the need to focus on usability and semantic interoperability is emphasized. Regarding technical issues, an emphasis on transparent and standards-based development processes is recommended, especially for landmark software projects. Finally, promoting mentality change and knowledge dissemination is also identified as key step toward the development of secure cross-border health data exchange services

Geospatial information infrastructures

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments

Wiki-health: from quantified self to self-understanding

Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Behavior life style analysis for mobile sensory data in cloud computing through MapReduce

Cloud computing has revolutionized healthcare in today's world as it can be seamlessly integrated into a mobile application and sensor devices. The sensory data is then transferred from these devices to the public and private clouds. In this paper, a hybrid and distributed environment is built which is capable of collecting data from the mobile phone application and store it in the cloud. We developed an activity recognition application and transfer the data to the cloud for further processing. Big data technology Hadoop MapReduce is employed to analyze the data and create user timeline of user's activities. These activities are visualized to find useful health analytics and trends. In this paper a big data solution is proposed to analyze the sensory data and give insights into user behavior and lifestyle trends

A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data

With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange