1,720,961 research outputs found
Detection of Adverse Drug Events from Social Media Texts – Research Project Overview
No full textThis paper presents an overview of the current research project on the detection of Adverse Drug Events from social media texts led by the Artificial Intelligence Laboratory of Udine (AILAB Udine). In the latest years, patients started reporting Adverse Drug Events (ADEs) on social media and similar online outlets, making it necessary to monitor them for pharmacovigilance purposes. For this reason, systems for the automatic extraction of ADEs are becoming an important research topic in the Natural Language Processing community. In this paper we present our research project, focused on the Detection, Extraction and Normalization of ADEs, detailing its objectives, achievements and future directions
Negation Detection for Robust Adverse Drug Event Extraction From Social Media Texts
Full text linkAdverse Drug Event (ADE) extraction from user-generated content has gained popularity as a tool to aid researchers and pharmaceutical companies to monitor side effect of drugs in the wild. Automatic models can rapidly examine large collections of social media texts. However it is currently unknown if such models are robust in face of linguistic phenomena such as negation and speculation, which are pervasive across language varieties. We evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: (i) a pipeline approach, using a specific component for negation detection; (ii) an augmentation of the dataset with artificially negated samples to further train the models. We show that both strategies bring significant increases in performance
Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation
No full textIn the last decade, an increasing number of users have started reporting adverse drug events (ADEs) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use natural language processing (NLP) techniques to rapidly examine these large collections of text, detecting mentions of drug-related adverse reactions to trigger medical investigations. However, despite the growing interest in the task and the advances in NLP, the robustness of these models in face of linguistic phenomena such as negations and speculations is an open research question. Negations and speculations are pervasive phenomena in natural language and can severely hamper the ability of an automated system to discriminate between factual and non-factual statements in text. In this article, we take into consideration four state-of-the-art systems for ADE detection on social media texts. We introduce SNAX, a benchmark to test their performance against samples containing negated and speculated ADEs, showing their fragility against these phenomena. We then introduce two possible strategies to increase the robustness of these models, showing that both of them bring significant increases in performance, lowering the number of spurious entities predicted by the models by 60% for negation and 80% for speculations
Extensive evaluation of transformer-based architectures for adverse drug events extraction
No full textAdverse Drug Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts. This task has been addressed by the Natural Language Processing community using large pre-trained language models, such as BERT. Despite the great number of Transformer-based architectures used in the literature, it is unclear which of them has better performances and why. Therefore, in this paper we perform an extensive evaluation and analysis of 19 Transformer-based models for ADE extraction on informal texts. We compare the performance of all the considered models on two datasets with increasing levels of informality (forums posts and tweets). We also combine the purely Transformer-based models with two commonly-used additional processing layers (CRF and LSTM), and analyze their effect on the models performance. Furthermore, we use a well-established feature importance technique (SHAP) to correlate the performance of the models with a set of features that describe them: model category (AutoEncoding, AutoRegressive, Text-to-Text), pre-training domain, training from scratch, and model size in number of parameters. At the end of our analyses, we identify a list of take-home messages that can be derived from the experimental data
A perspective on Artificial Intelligence for digital pharmacovigilance in pandemics
No full textDuring the COVID-19 pandemic, we witnessed the fastest massive vaccine rollout in human history. Like any other drug, vaccines have side effects, which might be harmful to the public trust when not properly communicated, hampering vaccination campaigns planned by governments. Therefore, it is of strategic importance to provide AI-empowered tools for monitoring their communication and feedback of social media users. At the same time, adverse drug event extraction from user-generated content has gained popularity as a tool to aid researchers and pharmaceutical companies in monitoring the side effects of drugs in a real-world setting. Using these innovative pharmacovigilance (PV) tools, it is possible to use natural language processing (NLP) techniques to monitor social media outlets and gain insights on how the population is reacting to information about the pandemic, vaccines, and measurement to contain infections, both from a psychological and physiological perspective. Data collected from social media allow us to draw comparisons between the different areas of the world, times, and living conditions, discovering meaningful patterns. Indeed, some of the socio-economic factors that characterize different geographical areas (e.g., general lifestyle, food choices, sports activities, ethnicity, economic status etc.) can have a tangible impact on the population's understanding and adherence to therapies, containing measures and more in general and diseases. In this article, we explore the possibilities of NLP for PV in the form of gathering and analyzing social media signals. We use as a case study the recent COVID-19 pandemic and show how the data collected on social media platforms such as Twitter since the start of the vaccination campaigns can provide useful insights and how this could help in future critical situations
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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