Сталий розвиток промислового регіону

У монографії визначено засади забезпечення сталого розвитку України та її промислових регіонів у контексті соціального та людського розвитку. Розроблено систему оцінки ризиків ресурсного забезпечення сталого розвитку. Розкрито вплив соціального капіталу на формування сталого розвитку. Визначено взяємозв’язок і взаємозалежність людського та сталого розвитку. Наведено теоретичну модель взаємозв’язку людського розвитку, нагромадження людського капіталу та підвищення конкурентоспроможності промислового регіону. Розкрито механізми активізації участі населення у забезпеченні сталого розвитку промислового регіону

E-Health

International audienceE-health is a large domain of research and applications of Information and Communication Technologies (ICT), not only in Medicine, but in the broad field of healthcare, including homecare and personalised health. The history of e-health started as soon as the 1960s, but e-health continues to extend its range of innovation and applications, particularly in developing countries and in the homecare domain. E-Health scientific background is based upon the theories of “Computer-Supported Cooperative Work” theorised by Schmidt, Ellis, and Johansen, in the 1990s. In this chapter, we present different fields of development of telemedicine, and Home-based tele-health. We present also how e-health contributes to the constitution of large networked data warehouses to be now exploited with the relevant methods

Leveraging hospital big data to monitor flu epidemics

International audienceBackground and objective - Influenza epidemics are a major public health concern and require a costly and time-consuming surveillance system at different geographical scales. The main challenge is being able to predict epidemics. Besides traditional surveillance systems, such as the French Sentinel network, several studies proposed prediction models based on internet-user activity. Here, we assessed the potential of hospital big data to monitor influenza epidemics.Methods - We used the clinical data warehouse of the Academic Hospital of Rennes (France) and then built different queries to retrieve relevant information from electronic health records to gather weekly influenza-like illness activity.Results - We found that the query most highly correlated with Sentinel network estimates was based on emergency reports concerning discharged patients with a final diagnosis of influenza (Pearson's correlation coefficient (PCC) of 0.931). The other tested queries were based on structured data (ICD-10 codes of influenza in Diagnosis-related Groups, and influenza PCR tests) and performed best (PCC of 0.981 and 0.953, respectively) during the flu season 2014-15. This suggests that both ICD-10 codes and PCR results are associated with severe epidemics. Finally, our approach allowed us to obtain additional patients' characteristics, such as the sex ratio or age groups, comparable with those from the Sentinel network.Conclusions - Conclusions: Hospital big data seem to have a great potential for monitoring influenza epidemics in near real-time. Such a method could constitute a complementary tool to standard surveillance systems by providing additional characteristics on the concerned population or by providing information earlier. This system could also be easily extended to other diseases with possible activity changes. Additional work is needed to assess the real efficacy of predictive models based on hospital big data to predict flu epidemics.<br

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