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Social-sanitary big data framework
No full textThe growing number of medical research on the web shows that the health and healthcare are increasingly digital. In order to ensure the safety of citizens, social cohesion, and economic competitiveness at a national level, the Information and Communication Technology (ICT) can facilitate knowledge and exchange of data through an analytical approach to problem solving, through continuous improvement of statistical managing of Big Data also in the healthcare field. The new technologies have become essential, thanks to the enormous possibilities that they can offer: we have witnessed over a very short period of time, that most of the human activities which were carried out manually have given space to much more efficient digital implementations. For instance, we can consider the serious problems that the vast documental archives have created in its management, and how centralized computer databases helped to solve most of these problems, speeding up and optimizing all research operations and data mining. This natural easiness of data exchange is still being expanded and facilitated by the development of computer networks, and in particular by the internet.
Despite progress made in recent years, the quality of the data is still one of the critical aspects of statistical production in the social-sanitary field and is partly due to the lack of accurate data provided by the peripheral structures, where the measurements are still in course of automation. Another factor that adversely affects the quality of the information consists of the delay time between the occurrence of the underlying events and the recording of related data: these, in fact, sometimes they are not inserted immediately in information systems and are then retrieved at a later time. ICT offers possible solutions, improving the administration and helping to streamline procedures and reduce costs. Moreover, the problems of data reliability, the provision of appropriate classifications in survey forms and, more generally, the quality of data are attributable, directly or indirectly, to the degree of computerization in social-sanitary production. In fact, in the presence of a fully computerized detection system, the possibility of errors transcription, manipulation and interpretation of the required information will drastically reduce (due to the non-perfect correspondence between the classification adopted in models of detection and what is recovered in the official records), as well as the time-lag in some cases considerable, between data recording and the actual time/instant of reference; on the other hand, the detailing of the information collected could increase a result of a greater and more appropriate articulation of the detection patterns (certainly not feasible, beyond a given limit, in cases of manual detection) and the activation of an automatic check on the consistency of the data would be possible, not only ex-post, but also during the same stage in which information is entered.
In reference to electronic health records, the Legislative Decree 179/2012 published in the Official Gazette of 11 November 2015 defines the set of health Big data and digital socio-sanitary documents generated from clinical present and past events: each one generates big data receiving a prescription, buying a medicine, requiring a health service, accessing to the emergency room, undergoing a diagnostic or laboratory examination, using social networks to communicate health conditions. Cross analysing this information, policy makers, hospitals and clinics could prevent the most common diseases and balance healthcare services according to the real needs of the population in a given territory. The application of these concepts to social-sanitary activities has opened a new and interesting line of research considered as a matter that unfolds on interoperability between the systems of public administration and the ICT. The crucial role of big data in healthcare is therefore to use the already considerable amount of existing information to avoid waste and to concentrate financial resources in sectors and medical specialties really needed by citizens
Struttura di comunità degli Ortotteri e integrità dei sistemi agropastorali del Sic-Zps Murgia Alta (Puglia, Italia)
No full textQualità del servizio e big data in I4.0 Grade Model : un applicazione di rete bayesiana
No full textOn the comparison of NN-based architectures for diabetic damage detection in retinal images
No full textThe automatic screening of retinal images for an early detection of diabetic symptoms and an early prevention of diabetic retinopathies has been a prime focus in recent times. In this paper a contribution to improve diabetic damage detection in retinal images via neural networks is proposed by comparing two neural strategies. By considering the first architecture, fundus oculi symptomatic pale regions are firstly highlighted by enhanc- ing image contrast with a neurofuzzy subnet, which is synthesized using a Sparsely- Connected Neural Network. Then, obtained contrast-enhanced images with bimodal histograms are globally segmented, after an optimal thresholding performed by a neural subsystem. In output binary images, suspect diabetic areas are finally isolated. By con- sidering the second architecture, an EBP MLP neural net is synthesized, where a suitable training set of suspect patterns is developed by (5 × 5) windows centered on damaged pixels in gold standard images provided by clinicians. Performances are evaluated by percentage measures of exactness in the detection of suspect damaged areas via a com- parison with gold standard images provided by clinicians. Results of both strategies are discussed and compared with other researchers’ ones
Struttura di comunità di ortotteri in relazione all’integrità dei sistemi agropastorali
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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