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

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Aug 1, 2020

Abstract

OBJECTIVE: 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.

Authors

David Oniani

Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA.
Guoqian Jiang

Mayo Clinic College of Medicine, Rochester, MN, USA.
Hongfang Liu

Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
Feichen Shen

Department of Health Sciences Research, Rochester MN.

Keywords

Algorithms Bayes Theorem Coronavirus Infections COVID-19 Datasets as Topic Decision Trees Humans Logistic Models Neural Networks, Computer Pandemics Pneumonia, Viral ROC Curve Software Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (32458963)

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

Abstract

Authors

Keywords

External Resources

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