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Constructing co-occurrence network embeddings to assist association extraction for COVID-19 and other coronavirus infectious diseases.
Journal of the American Medical Informatics Association ( IF 4.7 ) Pub Date : 2020-05-27 , DOI: 10.1093/jamia/ocaa117
David Oniani 1 , Guoqian Jiang 2 , Hongfang Liu 2 , Feichen Shen 2
Affiliation  

As coronavirus disease 2019 (COVID-19) started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 machine-readable dataset known as the COVID-19 Open Research Dataset (CORD-19) has been released. Based on this, our objective was to build a computable co-occurrence network embeddings to assist association detection among COVID-19–related biomedical entities.

中文翻译:

构建共现网络嵌入以辅助 COVID-19 和其他冠状病毒传染病的关联提取。

随着 2019 年冠状病毒病 (COVID-19) 开始迅速出现并逐渐转变为前所未有的大流行,因此对该疾病的知识库的需求变得至关重要。为了解决这个问题,我们发布了一个新的 COVID-19 机器可读数据集,称为 COVID-19 开放研究数据集 (CORD-19)。基于此,我们的目标是构建一个可计算的共现网络嵌入,以协助 COVID-19 相关生物医学实体之间的关联检测。
更新日期:2020-05-27
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