1,721,090 research outputs found
F. Ieva, Illusioni di potenza. La diplomazia sabauda e la Francia nel cuore del Seicento (1630-1648), Roma, Carocci, 2023
No full textReview of F. Ieva, Illusioni di potenza. La diplomazia sabauda e la Francia nel cuore del Seicento (1630-1648), Roma, Carocci, 2023, reviewed by Giacomo Carmagnin
Outlier detection for training sets in an unsupervised functional classification framework: an application to ECG signals
Full text linkA new method for unsupervised classification of multivariate functional data is proposed, and its application to ECG signals of PROMETEO (PROgetto sull’area Milanese Elettrocardiogrammi Teletrasferiti
dall’ Extra Ospedaliero) project is performed and discussed. In fact, classification is a challanging task
when data are curves, as well as outlier detection [1]. In particular, in the application to ECG signals
it makes possible a semi automatic diagnosis of cardiovascular diseases, based only on ECG traces morphology,
then not dependent on clinical evaluations. In [2], a real time procedure consisting of preliminary steps like reconstructing signals, wavelets denoising and removing biological variability in the signals through data registration is tuned and tested. Then, a multivariate functional k-means clustering of reconstructed and registered data is performed. Since the performances of classification method are to be validated through cross validation, it is mandatory a suitable training of the algorithm on data. Functional boxplots [4] and a multivariate extension of depth for functional data [3] are then used to detect outliers. The main focus of this work is then the application of these procedures to increase the algorithm predictive power robustifying selection criteria of data to be included in the training set, and then to perform an unsupervised multivariate functional
classification of ECG signals, based on the sole ECG’s morphology
Risk prediction for myocardial infarction via generalized functional regression models
No full textIn this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose prehospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques
A semiparametric bivariate probit model for joint modeling of outcomes in STEMI patients
Full text linkIn this work we analyse the relationship among in-hospital mortality and a treatment effectiveness outcome in patients affected by ST-Elevation myocardial infarction. The main idea is to carry out a joint modeling of the two outcomes applying a Semiparametric Bivariate Probit Model to data arising from a clinical registry called STEMI Archive. A realistic quantification of the relationship between outcomes can be problematic for several reasons. First, latent factors associated with hospitals organization can affect the treatment efficacy and/or interact with patient’s condition at admission time. Moreover, they can also directly influence the mortality outcome. Such factors can be hardly measurable. Thus, the use of classical estimation methods will clearly result in inconsistent or biased parameter estimates. Secondly, covariate-outcomes relationships can exhibit nonlinear patterns. Provided that proper statistical methods for model fitting in such framework are available, it is possible to employ a simultaneous estimation approach to account for unobservable confounders. Such a framework can also provide flexible covariate structures and model the whole conditional distribution of the response
Multinomial Multilevel Models with Discrete Random Effects: a Multivariate Clustering Tool
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
Polimi at CLinkaRT: a Conditional Random Field vs a BERT-based approach
Full text linkIn the context of the EVALITA 2023 challenge, we present the models we have developed for the CLinkaRT task, which aims to identify medical examinations and their corresponding results in Italian clinical documents. We propose two distinct approaches: one utilising a Conditional Random Field (CRF), a probabilistic graphical model traditionally used for Named Entity Recognition, and the other based on BERT, the transformer-based model that is currently state-of-the-art for many Natural Language Processing tasks. Both models incorporate external knowledge from publicly available medical resources and are enhanced with heuristic rules to establish associations between exams and results. Our comparative analysis elects the CRF-based model as the winner, securing the third position in the competition ranking, but the BERT-based model demonstrated competitive performance
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
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