Analyzing the vast coronavirus literature with CoronaCentral.
Analyzing the vast coronavirus literature with CoronaCentral.
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DOI：
10.1073/pnas.2100766118
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发表时间：
2021-06-08
影响因子：
11.1
通讯作者：
Altman RB
中科院分区：
文献类型：
--
作者：
Lever J;Altman RB
SARS-CoV-2大流行引起了对该病毒及其对人类健康影响的各个方面的研究激增。压倒性的出版率意味着研究人员无法跟上文献的步伐。为了改善这一点，我们提出了CoronaCentral资源，该资源使用机器学习来处理SARS-CoV-2以及SARS-CoV和MERS-CoV的研究文献。我们将文献分类为有用的主题和文章类型，并结合Altmetric数据分析危机期间的研究内容，速度和重点。这些主题包括治疗学、疾病预测，以及“长期COVID”和不平等研究等不断增长的领域。该资源可在https://coronacentral.ai上获得，每天更新。
The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as “long COVID” and studies of inequality. This resource, available at https://coronacentral.ai, is updated daily.
影响因子：
6.5
作者：
Doanvo, Anhvinh;Qian, Xiaolu;Majumder, Maimuna
通讯作者：
Majumder, Maimuna
影响因子：
14.9
作者：
Wei, Chih-Hsuan;Allot, Alexis;Lu, Zhiyong
通讯作者：
Lu, Zhiyong
DOI：
10.1093/jamia/ocaa091
发表时间：
2020-09-01
影响因子：
6.4
作者：
Roberts, Kirk;Alam, Tasmeer;Hersh, William R.
通讯作者：
Hersh, William R.