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      Big Data, Analyzing and Modelling: New Ways of Health Improvement and Regional Aspects

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          Abstract

          The field of health improvement and life prolonging develops poorly, despite all the advances in medicine, chemistry and genetic engineering. Among the main problems is the difficulty of using new scientific achievements in other industries due to the rapid development of specialized knowledge, the problem of returning costs for the creation of really effective and the problem of aging population in developed countries. There are problems with data for this methods usage with privacy and security on different levels with regional peculiarities. Effective timing of work on health at the personal level can result as a result of increased time and productivity. But it's difficult for people to allocate their intellectual resources for that, so you have to connect artificial intelligence and machine learning. Big Data model with methods and analysis techniques on different levels for health improvement was suggested. The importance of the level of social networks and its regional aspects for the analysis of health improvement data was identified. Big data processing results implementation and levels of interaction with human with request for changes model was proposed. It consists from two levels of interaction with humans by level of quick reaction and discussion with smart personal assistance. Regional aspects from possible AI implementation in undeveloped countries were analyzed on example of personal level big data for health usage.

          Translated abstract

          The field of health improvement and life prolonging develops poorly, despite all the advances in medicine, chemistry and genetic engineering. Among the main problems is the difficulty of using new scientific achievements in other industries due to the rapid development of specialized knowledge, the problem of returning costs for the creation of really effective and the problem of ageing population in developed countries. There are problems with data for this methods usage with privacy and security on different levels with regional peculiarities. Effective timing of work on health at the personal level can result as a result of increased time and productivity. But it's difficult for people to allocate their intellectual resources for that, so you have to connect artificial intelligence and machine learning. Big Data model with methods and analysis techniques on different levels for health improvement was suggested. The importance of the level of social networks and its regional aspects for the analysis of health improvement data was identified. Big data processing results implementation and levels of interaction with human with request for changes model was proposed. It consists from two levels of interaction with humans by level of quick reaction and discussion with smart personal assistance. Regional aspects from possible AI implementation in undeveloped countries were analyzed on example of personal level big data for health usage.

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          Mining association rules between sets of items in large databases

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            New Avenues in Opinion Mining and Sentiment Analysis

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              Innovative health service delivery models in low and middle income countries - what can we learn from the private sector?

              Background The poor in low and middle income countries have limited access to health services due to limited purchasing power, residence in underserved areas, and inadequate health literacy. This produces significant gaps in health care delivery among a population that has a disproportionately large burden of disease. They frequently use the private health sector, due to perceived or actual gaps in public services. A subset of private health organizations, some called social enterprises, have developed novel approaches to increase the availability, affordability and quality of health care services to the poor through innovative health service delivery models. This study aims to characterize these models and identify areas of innovation that have led to effective provision of care for the poor. Methods An environmental scan of peer-reviewed and grey literature was conducted to select exemplars of innovation. A case series of organizations was then purposively sampled to maximize variation. These cases were examined using content analysis and constant comparison to characterize their strategies, focusing on business processes. Results After an initial sample of 46 studies, 10 case studies of exemplars were developed spanning different geography, disease areas and health service delivery models. These ten organizations had innovations in their marketing, financing, and operating strategies. These included approaches such a social marketing, cross-subsidy, high-volume, low cost models, and process reengineering. They tended to have a narrow clinical focus, which facilitates standardizing processes of care, and experimentation with novel delivery models. Despite being well-known, information on the social impact of these organizations was variable, with more data on availability and affordability and less on quality of care. Conclusions These private sector organizations demonstrate a range of innovations in health service delivery that have the potential to better serve the poor's health needs and be replicated. There is a growing interest in investing in social enterprises, like the ones profiled here. However, more rigorous evaluations are needed to investigate the impact and quality of the health services provided and determine the effectiveness of particular strategies.
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                Author and article information

                Contributors
                Ukraine URI : http://lf.diit.edu.ua/
                Ukraine URI : http://www.lubp.com.ua/
                Ukraine URI : http://www.lubp.com.ua/
                Ukraine URI : http://www.lubp.com.ua/
                Journal
                Path of Science
                Altezoro, s.r.o. & Dialog
                31 August 2018
                : 2017-2023
                Affiliations
                [1 ]Lviv Branch of Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan
                [2 ]Lviv University of Business and Law
                Article
                10.22178/pos.37-2
                4ddf9b73-dd90-4384-acf6-c6fbe323d5f2

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History

                Education,Literary studies,Arts,Social & Behavioral Sciences,Economics
                big data,health improving,modelling,analyzing,regional aspects

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