1,720,980 research outputs found
New approaches on statistical modeling for drug safety data
Full text linkGli eventi avversi associati ai farmaci sono una delle principali cause di malattia e decesso al mondo e la statistica è, da sempre, uno strumento essenziale per contrastarli. Questa tesi si concentra sull'insieme di modelli e tecniche statistiche usate in fase di farmacovigilanza, ovvero l'individuazione degli effetti avversi di farmaci dopo che questi sono stati approvati e messi sul mercato. Nella prima parte della tesi verrà descritto il processo che porta all'approvazione di un farmaco da parte delle autorità di farmacovigilanza. Successivamente, si illustra il sistema di raccolta dati tipico della farmacovigilanza, basato sulla raccolta spontanea delle segnalazioni degli effetti collaterali di farmaci. Verranno quindi passati in rassegna e commentati i modelli statistici principalmente usati nell'analisi dei dati spontanei (noti come modelli di disproporzionalità) presenti in letteratura. Nella seconda parte della tesi verrà proposto un nuovo modello per l'analisi dei dati di farmacovigilanza. Questo modello, basato su una regressione con penalizzazione lasso, è stato pensato per analizzare i dati di farmacovigilanza e trovare nuove associazione fra farmaci ed effetti avversi, includendo anche le interazioni fra i farmaci, che possono a loro volta provocare degli effetti avversi. Il modello è stato testato sia su dati simulati che su dati reali. Nella terza parte della tesi si discute di un nuovo approccio alla statistica applicata alla farmacovigilanza. Si mostra come la capacità di trovare nuove associazione fra farmaci ed eventi avversi può essere incrementata includendo le informazioni provenienti dalla struttura biochimica dei farmaci. In particolare, sono state usate tecniche proprie del \textit{natural langue processing} per proiettare un farmaco in uno spazio di variabili latenti che ne descrive le caratteristiche biochimiche. L'utilizzo di queste variabili latenti, se debitamente affiancato ai dati spontanei, può essere un punto di svolta nelle procedure di farmacovigilanza.Adverse events associated with drugs are one of the leading causes of morbidity and mortality in the world, and statistics has always been an essential tool to contrast them. In this thesis, we focus on the set of statistical models and techniques used in pharmacovigilance, i.e. the detection of adverse effects of drugs after they have been approved and placed on the market. The first part of this thesis will describe the process that results in the approval of a drug by pharmacovigilance authorities. Next, the typical pharmacovigilance data collection system, based on the spontaneous report of adverse drug events, will be illustrated. The statistical models used mainly in the analysis of spontaneous data (known as disproportionality models) in the literature will then be reviewed and commented on. In the second part of the thesis, a new model for pharmacovigilance data will be proposed. This model, based on a lasso-penalized regression, is designed to analyze pharmacovigilance data and find new associations between drugs and adverse drug events, including drug-drug interactions, that may cause adverse events themselves. The model was tested on both simulated and real data. In the third part of the thesis, a new approach to statistics applied to pharmacovigilance is discussed.
We show how the ability to find new associations between drugs and adverse events can be increased by including information from the biochemical structure of drugs. Specifically, techniques peculiar to natural language processing were used to map a drug into an embedding space of latent variables that describes its biochemical characteristics. The use of these latent variables, when properly combined with spontaneous data, can be a turning point in pharmacovigilance procedures
Efectos cognitivos del lenguaje inclusivo en español: el procesamiento de @ y e en aprendientes de ELE.
No full textEl lenguaje inclusivo surge como una herramienta de lucha social que revindica la visibilización en el discurso de las mujeres y de las personas que no se identifican con el sistema cisgénero. Así surgen nuevas grafías que se pretenden imponer desde abajo como alternativas al uso genérico del masculino. No obstante, las marcas inclusivas como @ y e no quedan exentas de efectos en el procesamiento de la información, como se demuestra en el presente estudio experimental de eyetracking realizado con 88 aprendientes de español como lengua extranjera
Prediction of Italians’ Sentiment During the First COVID-19 Lockdown Through a Weighted Random Forest Balanced with SMOTE Algorithm
No full textDuring the first period of the COVID-19 lockdown in Italy, an online survey was spread through social networks as part of the SEBCOV international study to investigate the impact of the pandemic on Italians’ everyday life. The final optional question of the survey was open-ended, soliciting additional comments and garnered a remarkably high response rate. This particularly rich source of spontaneous insights about Italians’ feelings in this challenging period was classified manually into positive, negative, and neutral sentiment. In the previous work, we analyzed the sentiment expressed in these open-ended response texts, obtaining interesting results regarding the sentiment of Italians during the COVID-19 lockdown. In this article, we use survey questions to predict the sentiment of the participants who did not express it in the free text. To do so, we use a random forest model, trained on data balanced via the Synthetic Minority Over-sampling TEchnique (SMOTE) algorithm. The sample is weighted with the inverse of the probability of answering the open question, previously estimated with a logistic model. The results obtained allow us not only to predict the sentiment of subjects who did not express it but also to observe which survey questions are most associated with the sentiment
Bayesian mapping of mortality clusters
Full text linkDisease mapping analyses the distribution of several disease outcomes within a territory. Primary goals include identifying areas with unexpected changes in mortality rates, studying the relation among multiple diseases, and dividing the analysed territory into clusters based on the observed levels of disease incidence or mortality. In this work, we focus on detecting spatial mortality clusters, that occur when neighbouring areas within a territory exhibit similar mortality levels due to one or more diseases. When multiple causes of death are examined together, it is relevant to identify not only the spatial boundaries of the clusters but also the diseases that lead to their formation. However, existing methods in literature struggle to address this dual problem effectively and simultaneously. To overcome these limitations, we introduce perla, a multivariate Bayesian model that clusters areas in a territory according to the observed mortality rates of multiple causes of death, also exploiting the information of external covariates. Our model incorporates the spatial structure of data directly into the clustering probabilities by leveraging the stick-breaking formulation of the multinomial distribution. Additionally, it exploits suitable global-local shrinkage priors to ensure that the detection of clusters depends on diseases showing concrete increases or decreases in mortality levels, while excluding uninformative diseases. We propose a Markov chain Monte Carlo algorithm for posterior inference that consists of closed-form Gibbs sampling moves for nearly every model parameter, without requiring complex tuning operations. This work is primarily motivated by a case study on the territory of a local unit within the Italian public healthcare system, known as ULSS6 Euganea. To demonstrate the flexibility and effectiveness of our methodology, we also validate perla with a series of simulation experiments and an extensive case study on mortality levels in U.S. counties
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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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