In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.
This paper considers the affordances of social networking theories and tools to build new and effective e-learning practices. We argue that "connectivism" (social networking applied to learning and knowledge contexts) can lead to a reconceptualization of learning in which formal, non-formal and informal learning can be integrated as to build a potentially lifelong learning activities to be experienced in "personal learning environments". In order to provide a guide in the design, development and improvement both of personal learning environments and in the related learning activities we provide a knowledge flow model highlighting the stages of learning and the related enabling conditions. The derived model is applied in a possible scenario of formal learning in order to show how the learning process can be designed according to the presented theory.
In 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.
The current debate around the future of the Internet has brought to front the concept of "Content-Centric" architecture, lying between the Web of Documents and the generalized Web of Data, in which explicit data are embedded in structured documents enabling the consistent support for the direct manipulation of information fragments. In this paper we present the InterDataNet (IDN) infrastructure technology designed to allow the RESTful management of interlinked information resources structured around documents. IDN deals with globally identified, addressable and reusable information fragments; it adopts an URI-based addressing scheme; it provides a simple, uniform Web-based interface to distributed heterogeneous information management; it endows information fragments with collaboration-oriented properties, namely: privacy, licensing, security, provenance, consistency, versioning and availability; it glues together reusable information fragments into meaningful structured and integrated documents without the need of a pre-defined schema.
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