ArticleJournal of information science2022
An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation.
Article in Journal of information science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- Review
- Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles.Science advances · 2024Article
- Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.PloS one · 2024Article
- Mining the Health Disparities and Minority Health Bibliome: A Computational Scoping Review and Gap Analysis of 200,000+ Articles.medRxiv : the preprint server for health sciences · 2023Article
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
- Toward the design of ultrahigh-entropy alloys via mining six million texts.Nature communications · 2023Article
- A macro perspective of the perceptions of the education system via topic modelling analysis.Multimedia tools and applications · 2023Article
- Changes in the use of mobile devices during the crisis: Immediate response to the COVID-19 pandemic.Computers in human behavior reports · 2022Article
- Identifying Topics and Evolutionary Trends of Literature on Brain Metastases Using Latent Dirichlet Allocation.Frontiers in molecular biosciences · 2022Article
- Detecting Latent Topics and Trends in Global Publications on Brucellosis Disease Using Text Mining.Interdisciplinary perspectives on infectious diseases · 2022Article
- Approaches for text mining of mHealth literature.mHealth · 2022Article
- Research lines on the impact of the COVID-19 pandemic on business. A text mining analysis.Journal of business research · 2021Article
- A novel corpus-based computing method for handling critical word-ranking issues: An example of COVID-19 research articles.International journal of intelligent systems · 2021Article
- ScanBious: Survey for Obesity Genes Using PubMed Abstracts and DisGeNET.Journal of personalized medicine · 2021Article
- A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research.IEEE access : practical innovations, open solutions · 2021Article
- Research on Covid-19: a disruptive phenomenon for bibliometrics.Scientometrics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying text mining technique with latent Dirichlet allocation procedure to perform an overview of a mass of coronavirus literature. This overview presents the generic research themes of the coronavirus diseases: COVID-19, MERS and SARS, reveals the representative literature per main research theme and displays a network visualisation to explore the overlapping, similarity and difference among these themes. The overview can help the health and medical communities to extract useful information and interrelationships from coronavirus-related studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.