Constructing co-occurrence network embeddings to assist association extraction for COVID-19 and other coronavirus infectious diseases | Zendy

David Oniani | Zendy; Guoqian Jiang | Zendy; Hongfang Liu | Zendy; Feichen Shen | Zendy

Open Access

Constructing co-occurrence network embeddings to assist association extraction for COVID-19 and other coronavirus infectious diseases

Author(s) -

David Oniani,

Guoqian Jiang,

Hongfang Liu,

Feichen Shen

Publication year - 2020

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocaa117

Subject(s) - random forest , computer science , cluster analysis , artificial intelligence , naive bayes classifier , support vector machine , data mining , artificial neural network , machine learning , covid-19 , infectious disease (medical specialty) , medicine , disease , pathology

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.

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