Focus on: New trends, challenges and perspectives on healthcare cognitive computing: from information extraction to healthcare analytics
| Autor: | Coccoli, Mauro, Maresca, Paolo, Gabriella Tognola |
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| Přispěvatelé: | Coccoli, Mauro, Maresca, Paolo, Tognola, Gabriella |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | JE-LKS. Journal of E-Learning and Knowledge Society (Testo stamp.) 14 (2018): 7–9. info:cnr-pdr/source/autori:Coccoli, Mauro; Maresca, Paolo; Tognola, Gabriella/titolo:Focus on: New trends, challenges and perspectives on healthcare cognitive computing: from information extraction to healthcare analytics/doi:/rivista:JE-LKS. Journal of E-Learning and Knowledge Society (Testo stamp.)/anno:2018/pagina_da:7/pagina_a:9/intervallo_pagine:7–9/volume:14 Web of Science |
| Popis: | The focus of this special issue is cognitive computing in healthcare, due to the ever-increasing interest it is gaining for both research purposes and clinical applications. Indeed, cognitive computing is a challenging technology in many fields of application (Banavar, 2016) such as, e.g., medicine, education or eco- nomics (Coccoli et al., 2016) especially for the management of huge quantities of information where cognitive computing techniques push applications based on the use of big data (Coccoli et al., 2017). An unprecedented amount of data is made available from a heterogeneous variety of sources and this is true also in the case of health data, which can be exploited in many ways by means of sophisticated cognitive computing solutions and related technologies, such as, e.g., information extraction, natural language processing, and analytics. Also, from the point of view of programming they set challenging issues (see, e.g., Coccoli et al., 2015). In fact, the amount of healthcare that is now available and, potentially useful to care teams, reached 150 Exabytes worldwide and about 80% of this huge volume of data is in an unstructured form, being thus somehow invisible to systems. Hence, it is clear that cognitive computing and data analytics are the two key factors we have for make use – at least partially – of such a big volume of data. This can lead to personalized health solutions and healthcare systems that are more reliable, effective and efficient also re- ducing their expenditures. Healthcare will have a big impact on industry and research. However, this field, which seems to be a new era for our society, requires many scientific endeavours. Just to name a few, you need to create a hybrid and secure cloud to guarantee the security and confidentiality of health data, especially when smartphones or similar devices are used with specific app (see, e.g., Mazurczyk & Caviglione, 2015). Beside the cloud, you also need to consider novel ar- chitectures and data platforms that shall be different from the existing ones,because 90% of health and biomedical data are images and also because 80% of health data in the world is not available on the Web. This special issue wants to review state-of-the-art of issues and solutions of cognitive computing, focusing also on the current challenges and perspecti- ves and includes a heterogeneous collection of papers covering the following topics: information extraction in healthcare applications, semantic analysis in medicine, data analytics in healthcare, machine learning and cognitive com- puting, data architecture for healthcare, data platform for healthcare, hybrid cloud for healthcare. |
| Databáze: | OpenAIRE |
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