ABSTRACT
Artificial-Intelligence (AI) is becoming more widespread in several areas, from economics and government to consumer-services and even healthcare. In fact, in the latter, there was a big use increase in the past three years, also due to the COVID-19 pandemic. Several solutions have been implemented to tackle the several challenges imposed by this new disease, being one of such solutions chatbots. In this article, we present the results of a Systematic Literature Review (SLR) that identifies the Chatbots applications in COVID-19 disease. In this SLR, we identified 9987 papers from which we selected 30 studies, on which we performed a full-text analysis. From our research, we could conclude that several solutions were implemented, with good acceptance by citizens, despite several limitations, such as limited time to develop the solutions (which narrowed some features, such as AI voice conversation), lack of global implementation and infrastructure limitations. © 2022 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.
ABSTRACT
The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19. © ACL 2020.All right reserved.
ABSTRACT
Objective: The mechanism of action, metabolic kinetics, efficacy, safety and drug-drug interaction of molnupiravir were reviewed to provide a basis for clinical use. Method(s): Literature related to molnupiravir was systematically searched in Chinese Clinical Trial Registry, clinicaltrials.gov, Pubmed, Chinese Journal Full-text Database (CNKI) and Wanfang database, and the relevant information was reviewed. Results & Conclusion(s): Molnupiravir was the world's first small-molecule oral drug for COVID-19, which had been approved or authorized for emergency use in more than 40 countries all over the world. Molnupiravir was a ribonucleoside analogue that could be caused mutations in RNA products by viral RNA polymerase, and thus halt viral replication. Clinical trial results showed that molnupiravir could be reduced hospitalization and mortality rates in patients with mild and moderate COVID-19, and might be effective against SARS-CoV-2 mutant strains.Molnupiravir had good safety and tolerability, to provide reference for the treatment of COVID-19 in the future. Copyright © 2022, Chinese Journal of New Drugs Co. Ltd. All right reserved.
ABSTRACT
To support the development of entity recognition tools, this study manually annotates 99 full-text articles about COVID-19. Each article is annotated by 6 annotators through two rounds. 18 types of entity are involved, including genes, diseases, chemicals, coronaviruses and so on. We also calculate the inter-annotator agreement (IAA) scores in term of multi-κ measure to ensure the quality of the annotations. In the end, 39, 118 entity mentions are manually annotated in total. © Copyright 2022 for this paper by its authors.
ABSTRACT
Dimensions was built as a platform to allow stakeholders in the research community, including academic bibliometricians, to more easily create and understand the context of different types of research object through the linkages between these objects. Links between objects are created via persistent identifiers and machine learning techniques, while additional context is introduced via data enhancements such as per-object categorisations and person and institution disambiguation. While these features make analytical use cases accessible for end users, the COVID-19 crisis has highlighted a different set of needs to analyze trends in scholarship as they occur: Real-time bibliometrics. The combination of full-text search, daily data updates, a broad set of scholarly objects including pre-prints and a wider set of data fields for analysis, broadens opportunities for a different style of analysis. A subset of these emerging capabilities is discussed and three basic analyses are presented as illustrations of the potential for real-time bibliometrics.