ABC: A knowledge Based Collaborative framework for e-health

Amato, Flora; Cozzolino, Giovanni; Maisto, Alessandro; Mazzeo, Antonino; Moscato, Vincenzo; Pelosi, Serena; Picariello, Antonio; Romanò, Sara; Sansone, Carlo

doi:10.1109/rtsi.2015.7325107

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…2 Knowledge based approaches: These methods include defining of perception, decision and action mechanism in order to create a business outcome. There are numerous studies on knowledge-based approach in the literature [201][202][203][204][205].…”

Section: Discussionmentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

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Section: Discussionmentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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“…Amato et al (2015) introduced a knowledge-based collaborative system (ABC), which has two settings: an electronic clinical decision support system (CDSS) and an offline medical data checking and computing mechanism. The CDSS will support health-care users while drafting records related to their work.…”

Section: Review Of Selected Articlesmentioning

confidence: 99%

“…The study aimed at integrating explicit data and implicit professional experiences in different settings into a knowledge material suitable for supporting the work of the places where it is to be used. Amato et al (2015) introduced a knowledge-based collaborative system (ABC), which has two settings: an electronic clinical decision support system (CDSS) and an offline medical data checking and computing mechanism. The CDSS will support health-care users while drafting records related to their work.…”

Section: Provided Frameworkmentioning

confidence: 99%

The impact of knowledge on e-health: a systematic literature review of the advanced systems

Hosseinzadeh

Ahmed

et al. 2020

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Purpose Economists have recognized knowledge management as a promising tool regarding all aspects of the economy, including health care. The volume of biomedical literature is currently growing at an exponential rate, and the vast number of studies makes it extremely difficult for researchers to keep up with new developments in their research areas. Therefore, the efficient management of huge amounts of data and the accuracy of the knowledge thus obtained are vital concerns. Electronic health (e-health) has emerged as a useful concept to provide data for solo self-care management. Although health care is a common topic on the internet, patients rarely share their health care-related knowledge on social media. This study aims to examine the impact of knowledge on e-health. Design/methodology/approach This paper complies with the methodological requirements of systematic literature reviews. The present paper has investigated the newest systems and studied their practical techniques in detail. The effects of knowledge on e-health have been categorized into major groups. Findings The outcomes indicate that the capabilities of information and communication technology certainly promote the exchange of knowledge within clinics. The results also show that institutional architectures have significant impacts on knowledge-sharing exercises, significantly improving patient safety. Practical implications These findings will be essential in the understanding of the interplay among various signals in theory and in the understanding of patients’ choice in the e-health community in practice. The results have implications for existing health management and e-health literature. The present paper will help policymakers, health-care executives and project managers to effectively set their operations and make them maintainable, prevent unpredicted obstacles and better allocate their resources. Overall, the results of this paper will guide researchers who are working in the field of e-health. Originality/value E-health attempts have mostly focused on answering questions using context-specific technical answers, regardless of the key role of knowledge resources. The present paper has provided an innovative viewpoint on how knowledge resources and knowledge-sharing initiatives may have a role in the innovative work behaviors shown by health-care employees. As noted before, there have been only a few studies regarding the effects of knowledge on health care, so the present paper contributes to the previous literature, particularly about e-health.

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“…Analyse the results of thousands of researchers around the world about the Covid-19 is not an easy task for anyone, and it would be far easier if there could be a summary of the main points reached by the scientific community [1].…”

Section: Introductionmentioning

confidence: 99%

Analysis of COVID-19 Data

Amato

Cozzolino

Maisto

et al. 2020

Advances on P2P, Parallel, Grid, Cloud and Internet Computing

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A lot of research has been done during the first months of 2020 regarding the Covid-19. Researchers of different fields worked and cooperated to understand the virus better, in order to manage the pandemic and to model its spread. A series of tools have been developed in this sense, but there is a lack of work with regards to what has been developed from the scientific community. We would like to, at least partially, summarise the results obtained so far by analysing some of the published papers on the matter. To achieve such a result, we are going to use different python libraries that allow analysing texts. The entire work has been done with python on the Google Colaboratory platform.

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ABC: A knowledge Based Collaborative framework for e-health

Cited by 9 publications

References 27 publications

Analysis of factors affecting IoT-based smart hospital design

Analysis of factors affecting IoT-based smart hospital design

The impact of knowledge on e-health: a systematic literature review of the advanced systems

Analysis of COVID-19 Data

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