Visualising COVID-19 Research

• URL: http://arxiv.org/abs/2005.06380v2
• Date: Fri, 15 May 2020 10:06:39 GMT
• Title: Visualising COVID-19 Research
• Authors: Pierre Le Bras, Azimeh Gharavi, David A. Robb, Ana F. Vidal, Stefano Padilla, Mike J. Chantler
• Abstract summary: We develop a novel automated theme-based visualisation method. It combines advanced data modelling of large corpora, information mapping and trend analysis. It provides a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources.
• Score: 4.664989082015335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a new strain of coronavirus, causing the COVID-19 pandemic, and radically changing our lives and work conditions. Many scientists are working tirelessly to find a treatment and a possible vaccine. Furthermore, governments, scientific institutions and companies are acting quickly to make resources available, including funds and the opening of large-volume data repositories, to accelerate innovation and discovery aimed at solving this pandemic. In this paper, we develop a novel automated theme-based visualisation method, combining advanced data modelling of large corpora, information mapping and trend analysis, to provide a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources. We apply this method on two recently released publications datasets (Dimensions' COVID-19 dataset and the Allen Institute for AI's CORD-19). The results reveal intriguing information including increased efforts in topics such as social distancing; cross-domain initiatives (e.g. mental health and education); evolving research in medical topics; and the unfolding trajectory of the virus in different territories through publications. The results also demonstrate the need to quickly and automatically enable search and browsing of large corpora. We believe our methodology will improve future large volume visualisation and discovery systems but also hope our visualisation interfaces will currently aid scientists, researchers, and the general public to tackle the numerous issues in the fight against the COVID-19 pandemic.

Related papers

• COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics [116.6248556979572]
  COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data. It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
  arXiv  Detail & Related papers  (2021-03-18T03:31:33Z)
• Accelerating COVID-19 research with graph mining and transformer-based learning [2.493740042317776]
  We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time. We show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone.
  arXiv  Detail & Related papers  (2021-02-10T15:11:36Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 [75.84689958489724]
  The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19.
  arXiv  Detail & Related papers  (2020-07-04T22:48:15Z)
• Coronavirus Knowledge Graph: A Case Study [4.646516629534201]
  We use several Machine Learning, Deep Learning, and Knowledge Graph construction and mining techniques to identify COVID-19 related experts and bio-entities. We suggest possible techniques to predict related diseases, drug candidates, gene, gene mutations, and related compounds.
  arXiv  Detail & Related papers  (2020-07-04T03:55:31Z)
• The challenges of deploying artificial intelligence models in a rapidly evolving pandemic [10.188172055060544]
  We argue that both basic and applied research are essential to accelerate the potential of AI models. This perspective may provide a glimpse into how the global scientific community should react to combat future disease outbreaks more effectively.
  arXiv  Detail & Related papers  (2020-05-19T21:11:48Z)
• Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned [88.42878484408469]
  We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures. This paper describes our initial efforts and offers a few thoughts about lessons we have learned along the way.
  arXiv  Detail & Related papers  (2020-04-10T17:12:29Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.