1,720,971 research outputs found
Heart Failure analysis Dashboard for patient's remote monitoring combining multiple artificial intelligence technologies
No full textIn this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis
Random forest for automatic assessment of heart failure severity in a telemonitoring scenario
No full textIn this study, we describe an automatic classifier of patients with Heart Failure designed for a telemonitoring scenario, improving the results obtained in our previous works. Our previous studies showed that the technique that better processes the heart failure typical telemonitoring-parameters is the Classification Tree. We therefore decided to analyze the data with its direct evolution that is the Random Forest algorithm. The results show an improvement both in accuracy and in limiting critical errors
Induction Models and Teachers Professional Development
No full textThe initial training programmes for teacher induction to school activity need to be integrated within a recursive self-sustaining circularity, able
to produce professional skills. The present work analyses the models and perspectives that have characterized the professional development
in Induction experiences and defines an innovative theoretical framework underpinning the experience of Italian Newly Qualified Teachers (NQT)
in the 2014/2015 academic year, based on a pattern which envisages an alternation of theory-practice and reflection. Such a pattern finds in
the teacher’s portfolio, structured into training curriculum, teaching and competence assessment, a time of tight integration of the perspectives
that guide the teacher’s work, maximizing moments of action, reflection and appraisal. It will present methodologies and tools to investigate teachers’
perception about the role of the portfolio and whether it was a model capable of accompanying the new qualified teachers in an authentic way, letting teachers understand their competences and practice
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
A Tool for Patient Data Recovering Aimed to Machine Learning Supervised Training
No full textIn this paper we present a method and a tool to acquire data of outpatients suffering from Heart Failure and to populate a database so that it is suitable for the supervised training of machine learning techniques. In our studies we had to train an artificial intelligence-based system to recognize different severity and to predict worsening of heart failure patients, using as input various parameters acquirable during outpatient visits. We have therefore developed a tool that would allow the cardiologist to populate a ”supervised database” suitable for machine learning during his regular outpatient consultations. The idea comes from the fact that in literature there are few databases of this type and they are not scalable in our case. The tool includes a management part for the patient demographics, a part for the data acquisition, a part for displaying the follow-ups of a patient, a part of artificial intelligence to provide the smart output, and a part called ”score based prognosis” in which we have computerized some prognostic models known in the literature as the SHFM, the CHARM, the EFFECT, and ADHERE
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
A system to improve continuity of care in heart failure patients
No full textIn this paper we expose the design of a system for the remote monitoring of Heart Failure (HF) patients, complemented by an Artificial Intelligence (AI) engine to perform a classification of patients severity on a three levels scale: mild, moderate and severe. The system allows multiple care regimes: a scheme called IHC (Integrated Home Care) and a scheme called CIHC (Continuous Integrated Home Care). The first needs that a health care worker is traveling periodically to the patient's home to perform various measurements of physiological parameters, the second is fully automatic but requires that a kit for the automatic acquisition of the parameters is provided to the patient. In results section we show performances of AI, trained using our clinical partner database, in assessing HF severity and HF type that are respectively 89% and 86% hold out accuracy. This system would facilitate the application of the principles of the Chronic Care Model, in our case regarding the assistance for Heart Failure, but the system is scalable to many other chronic diseases. Due to the amount of input parameters and the fact that HF involves the whole body, we believe that it can be the right disease for the prototype of a disease-specialized system that allows structured communications between hospital and territory.In this paper we expose the design of a system for the remote monitoring of Heart Failure (HF) patients, complemented by an Artificial Intelligence (AI) engine to perform a classification of patients severity on a three levels scale: mild, moderate and severe. The system allows multiple care regimes: a scheme called IHC (Integrated Home Care) and a scheme called CIHC (Continuous Integrated Home Care). The first needs that a health care worker is traveling periodically to the patient's home to perform various measurements of physiological parameters, the second is fully automatic but requires that a kit for the automatic acquisition of the parameters is provided to the patient. In results section we show performances of AI, trained using our clinical partner database, in assessing HF severity and HF type that are respectively 89% and 86% hold out accuracy. This system would facilitate the application of the principles of the Chronic Care Model, in our case regarding the assistance for Heart Failure, but the system is scalable to many other chronic diseases. Due to the amount of input parameters and the fact that HF involves the whole body, we believe that it can be the right disease for the prototype of a disease-specialized system that allows structured communications between hospital and territory
Performance assessment of a Clinical Decision Support System for analysis of Heart Failure
No full textIn this paper we compare five machine learning techniques in dealing with typical Heart Failure (HF) data. We developed a Clinical Decision Support System (CDSS) for the analysis of Heart Failure patient that provides various outputs such as an HF severity evaluation, an HF type prediction, as well as a management interface that compares the various patient's follow-ups. To realize these smart functions we used machine learning techniques and in this paper we compare the performance of a neural network, a support vector machine, a system with fuzzy rules genetically produced, a Classification and regression tree and its direct evolution which is the Random Forest, in analyzing our database. Best performances (intended as accuracy and less critical errors committed) in both HF severity evaluation and HF type prediction functions are obtained by using the Random Forest algorithm. © Springer International Publishing Switzerland 2014
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