A Comprehensive Overview of the COVID-19 Literature: Machine Learning-Based Bibliometric Analysis.

ABSTRACT:

Background

Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging.

Objective

We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature.

Methods

We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning-based method to analyze the most re

SUBMITTER: Abd-Alrazaq A 

PROVIDER: S-EPMC7942394 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature