1,721,387 research outputs found
Manuale ECDL-H
No full textLe ICT sono sempre più presenti nella vita di tutti i giorni, sia privata che professionale ed il mondo della sanità non fa eccezione.Ancora per un po’ di anni gli operatori sanitari non proverranno dalla generazione dei cosiddetti “nativi digitali” ma nella migliore delle ipotesi da quella degli “emigranti digitali”. Questo significa che sempre più spesso gli operatori sanitari sono addestrati all’utilizzo di specifici programmi, ma più o meno inconsciamente tenderanno a considerarli la copia elettronica del corrispondente documento cartaceo o della corrispondente procedura manuale. Si rischia di perdere in questo modo il pieno senso della “virtualità” e delle sue implicazioni. Virtuale non è l’opposto di “reale”, ma indica un tipo diverso di realtà, svincolata da un luogo fisso e da un tempo determinato. Scrivere un’informazione su una cartella clinica cartacea, custodita in un classificatore in un reparto ospedaliero o in uno studio medico non è come scriverla in un archivio elettronico, specie se quest’ultimo è – anche solo potenzialmente – collegato in rete con altri archivi e computer. L’informazione in quest’ultimo caso si smaterializza, non è più in un luogo fisico preciso. Al contrario quell’informazione ha aumentato enormemente la sua fruibilità e quindi il numero ed il tipo di conseguenze che può generare. Lo scopo di questo libro è analizzare le conseguenze di tutto ciò sui processi di cura, sulle relazioni fra pazienti/utenti di un servizio sanitario ed operatori, sulle relazioni degli operatori fra loro o fra pazienti, operatori e sistema organizzativo sanitario nel suo complesso Esso nasce innanzi tutto dall’esigenza di promuovere questa consapevolezza fra tutti coloro che a vario titolo utilizzano sistemi informativi per la sanità. Quest’ultima espressione – sistema informativo – è infatti il concetto centrale intorno a cui ruotano tutti gli altri contenuti nel libro
Crash Prediction and Risk Assessment with Individual Mobility Networks
No full textThe massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we address the problem of building a data-driven model for predicting car drivers' risk of experiencing a crash in the long-Term future, for instance, in the next four weeks. Since the raw mobility data, although potentially large, typically lacks any explicit semantics or clear structure to help understanding and predicting such rare and difficult-To-grasp events, our work proposes to build concise representations of individual mobility, that highlight mobility habits, driving behaviors and other factors deemed relevant for assessing the propensity to be involved in car accidents. The suggested approach is mainly based on a network representation of users' mobility, called Individual Mobility Networks, jointly with the analysis of descriptive features of the user's driving behavior related to driving style (e.g., accelerations) and characteristics of the mobility in the neighborhood visited by the user. The paper presents a large experimentation over a real dataset, showing comparative performances against baselines and competitors, and a study of some typical risk factors in the areas under analysis through the adoption of state-of-Art model explanation techniques. Preliminary results show the effectiveness and usability of the proposed predictive approach
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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
City indicators for mobility data mining
No full textClassifying cities and other geographical units is a classical task in urban geography, typically carried out through manual analysis of specific characteristics of the area. The primary objective of this paper is to contribute to this process through the definition of a wide set of city indicators that capture different aspects of the city, mainly based on human mobility and automatically computed from a set of data sources, including mobility traces and road networks. The secondary objective is to prove that such set of characteristics is indeed rich enough to support a simple task of geographical transfer learning, namely identifying which groups of geographical areas can share with each other a basic traffic prediction model. The experiments show that similarity in terms of our city indicators also means better transferability of predictive models, opening the way to the development of more sophisticated solutions that leverage city indicators
TOSCA: TwO-Steps Clustering Algorithm for personal locations detection
No full textOne of the key tasks in mobility data analysis is the study of the individual mobility of users with reference to their per- sonal locations, i.e. The places or areas where they stop to perform any kind of activities. Correctly discovering such personal locations is therefore a very important problem, which is yet not very well addressed in literature. In this work we propose a robust, efficient, statistically well-founded and parameter-free personal location detection process. The algorithm, called TOSCA (TwO-Steps parameter free Clustering Algorithm), combines two clustering strategies and applies statistical tests to drive the selection of the needed parameters. The proposed solution is tested against a large set of competitors and several datasets, including synthetic and real ones. The empirical results show its ability to auto- matically adapt to different contexts yielding good accuracy and a good efficiency
Gross polluters and vehicle emissions reduction
Full text linkVehicle emissions produce an important share of a city’s air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing the full driving cycle of vehicles, or focus on a few vehicles. We have used GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in three European cities. We found that the emissions across the vehicles and roads are well approximated by heavy-tailed distributions and thus discovered the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are far more effective than those limiting circulation based on an uninformed choice of vehicles. Our study contributes to shaping the discussion on how to measure emissions with digital data
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