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

Secondary Use of Healthcare Structured Data The Challenge of Domain-Knowledge Based Extraction of Features

International audienceSecondary use of clinical structured data takes an important place in healthcare research. It was first described by Fayyad as "knowledge discovery in databases". Feature extraction is an important phase but received little attention. The objectives of this paper are 1) to propose an updated representation of data reuse in healthcare, 2) to illustrate methods and objectives of feature extraction, and 3) to discuss the place of domain-specific knowledge.MATERIAL AND METHODS an updated representation is proposed. Then, a case study consists of automatically identifying acute renal failure and discovering risk factors, by secondary use of structured data. Finally, a literature review published par Meystre et al. is analyzed.RESULTS 1) we propose a description of data reuse in 5 phases. Phase 1 is data preprocessing (cleansing, linkage, terminological alignment, unit conversions, deidentification), it enables to construct a data warehouse. Phase 2 is feature extraction. Phase 3 is statistical and graphical mining. Phase 4 consists of expert filtering and reorganization of statistical results. Phase 5 is decision making. 2) The case study illustrates how time-dependent features can be extracted from laboratory results and drug administrations, using domain-specific knowledge. 3) Among the 200 papers cited by Meystre et al., the first and last authors were affiliated to health institutions in 74% (68% for methodological papers, and 79% for applied papers).DISCUSSION features extraction has a major impact on success of data reuse. Specific knowledge-based reasoning takes an important place in feature extraction, which requires tight collaboration between computer scientists, statisticians, and health professionals