ArticleBMJ health & care informatics2021
Network graph representation of COVID-19 scientific publications to aid knowledge discovery.
Article in BMJ health & care informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data.Scientific data · 2024Article
- Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs.Frontiers in research metrics and analytics · 2023Article
- Current global research landscape on COVID-19 and cancer: Bibliometric and visualization analysis.World journal of clinical oncology · 2022Article
- The Relationship between Mustard Import and COVID-19 Deaths: A Workflow with Cross-Country Text Mining.Healthcare (Basel, Switzerland) · 2022Article
- Scientometric assessment of scientific documents published in 2020 on herbal medicines used for COVID-19.Journal of herbal medicine · 2022Article
- COVID-19-Related Scientific Literature Exploration: Short Survey and Comparative Study.Biology · 2022Review
- Mapping the landscape and structure of global research on nutrition and COVID-19: visualization analysis.Journal of health, population, and nutrition · 2022Article
- Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology.BMC medical informatics and decision making · 2022Article
- Comparison of Machine-Learning Algorithms for the Prediction of Current Procedural Terminology (CPT) Codes from Pathology Reports.Journal of pathology informatics · 2022Article
- Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape.Journal of personalized medicine · 2021Review
- Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding.International journal of environmental research and public health · 2021Article
- A topic trend analysis on COVID-19 literature.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionNumerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult.
methodsA graph network was constructed from the publicly available COVID-19 Open Research Dataset (CORD-19) of COVID-19-related publications using an engine leveraging medical knowledge bases to identify discrete medical concepts and an open-source tool (Gephi) to visualise the network.
resultsThe network shows connections between diseases, medications and procedures identified from the title and abstract of 195 958 COVID-19-related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledge base and the size of the node related to the number of publications containing the term. The data set and visualisations were made publicly accessible via a webtool.
conclusionKnowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity inter-relationships to improve understanding of diseases such as COVID-19.
Indexed as
Identifiers
What OpenQuestion holds
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.