Network graph representation of COVID-19 scientific publications to aid knowledge discovery

被引：16

作者：

Cernile, George ^{[1
]}

Heritage, Trevor ^{[1
]}

Sebire, Neil J. ^{[2
]}

Gordon, Ben ^{[2
]}

Schwering, Taralyn ^{[1
]}

Kazemlou, Shana ^{[1
]}

Borecki, Yulia ^{[1
]}

机构：

[1] Inspirata, Tampa, FL USA

[2] HDRUK, London, England

来源：

BMJ HEALTH & CARE INFORMATICS | 2021年 / 28卷 / 01期

关键词：

BMJ health informatics; health care; information science; medical informatics;

D O I：

10.1136/bmjhci-2020-100254

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Introduction Numerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult. Methods A 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. Results The 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. Conclusion Knowledge 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.

引用

页数：4

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