The European Union has a substantial investment in research and development and demand side-measures in the health sector in order to promote new initiatives, prevent disease and foster healthy lifestyles. In particular, the European Commission and other European entities have funded research projects focused on the use of technology in the health sector. In this context, health research initiatives have evolved from user-centred monolithic solutions into collaborative partnerships of different stakeholders that gather around different technological platforms. In order to identify the lacks and opportunities in this area, a systematic mapping study was conducted with the aim of identifying and analysing the recent research projects developed in Europe related to technological ecosystems in the health sector. The study covered closed European research projects from 2003 to 2018. This paper aims to extend that systematic mapping study through ongoing research projects. The analysis of these research projects provides an overview of the current trends and identify the lacks and opportunities to define new advances in this research area. Moreover, the comparison between the first mapping study focused on closed projects, and the current study, allows getting an overview of the evolution of technological ecosystems in the health sector.
Data mining; visualization; machine learning Nowadays, great amount of data is being created by several sources from academic, scientific, business and industrial activities. Such data intrinsically contains meaningful information allowing for developing techniques, and have scientific validity to explore the information thereof. In this connection, the aim of artificial intelligence (AI) is getting new knowledge to make decisions properly. AI has taken an important place in scientific and technology development communities, and recently develops computer-based processing devices for modern machines. Under the premise, the premise that the feedback provided by human reasoning-which is holistic, flexible and parallel-may enhance the data analysis, the need for the integration of natural and artificial intelligence has emerged. Such an integration makes the process of knowledge discovery more effective, providing the ability to easily find hidden trends and patterns belonging to the database predictive model. As well, allowing for new observations and considerations from beforehand known data by using both data analysis methods and knowledge and skills from human reasoning. In this work, we review main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this emergent field. As well, key aspects to conceptually compare them are provided.
In this document, a proposal is made to study the data that will be generated in the private and anonymous community of the WYRED project, in order to extract knowledge about how their users interact, both between them, and with the platform. To do this, it is started with the creation of a system that will generate a set of test data, as close as possible to the original. With this information and considering the impact of privacy when dealing with the data of the project, a flexible and complete architecture has been proposed for the development of interactive visualizations that will allow to visualize the previously generated data. Finally, a use case is presented where the suitability of the visual analytic is demonstrated to perform analysis of the data of the project and to extract knowledge, in a simple way. CCS CONCEPTS • Human-centered computing ➝ Human computer interaction (HCI) ➝ Interactive systems and tools
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